claim_id large_stringlengths 20 20 | paper_id large_stringlengths 17 20 | year int32 2.02k 2.03k | atomic_claim large_stringlengths 18 387 | claim_index int32 0 8 | extractor_model large_stringclasses 1
value | prompt_hash large_stringclasses 1
value | run_id large_stringclasses 2
values | created_at large_stringclasses 2
values |
|---|---|---|---|---|---|---|---|---|
clm_9640a72d73253957 | 2022.emnlp-main.262 | 2,022 | Spread estimates intrinsic few-shot hardness using feature-space invariances between training and test samples. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_96017a8e2ac86c85 | 2022.emnlp-main.262 | 2,022 | Spread accounts for intrinsic few-shot hardness better than existing metrics. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d5fe44533ee9703c | 2022.emnlp-main.262 | 2,022 | Spread is faster to compute than existing metrics for intrinsic few-shot hardness. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8328b69150eb9005 | 2023.emnlp-main.679 | 2,023 | FFLM enables zero-shot faithfulness evaluation for text summarization using a moderately-sized foundation language model. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_94b8495c70fdce43 | 2023.emnlp-main.679 | 2,023 | FFLM measures faithfulness by combining probability changes that occur when prefixing text consistent with the output. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_486c44bbb767a3a6 | 2023.emnlp-main.679 | 2,023 | FFLM achieves competitive or superior performance to ChatGPT on inconsistency detection and faithfulness rating with a substantially smaller model size. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f368c911cbde2a6f | 2020.emnlp-main.402 | 2,020 | Sequence ranking is the best training objective for in-domain pronoun resolution. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f98f437464252273 | 2020.emnlp-main.402 | 2,020 | Semantic similarity between candidates and pronoun is the best training objective for out-of-domain pronoun resolution. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c1b9f12903f994ca | 2020.emnlp-main.402 | 2,020 | Sequence ranking training objective exhibits seed-wise instability in pronoun resolution, but other training objectives do not. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_63a952970e94698d | 2021.emnlp-main.802 | 2,021 | XTREME-R is a multilingual natural language understanding benchmark with an improved set of ten tasks. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c145d666108a1207 | 2021.emnlp-main.802 | 2,021 | XTREME-R includes challenging language-agnostic retrieval tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2bd0915a6a3a5a4c | 2021.emnlp-main.802 | 2,021 | XTREME-R covers 50 typologically diverse languages. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f3eca0de4446b107 | 2021.emnlp-main.802 | 2,021 | The massively multilingual diagnostic suite diagnoses multilingual model behavior. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b82670f46a3bea44 | 2021.emnlp-main.802 | 2,021 | Fine-grained multi-dataset evaluation capabilities assess multilingual models. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2bb2fb461b167191 | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) leverages temporal cues from queries to adaptively prune video tokens while preserving contextual continuity and reducing computational overhead. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_05bf9920eebb57a5 | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) retains higher token density in temporally relevant segments compared to uniform pruning and keyframe selection. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c0e2d15e6e0fc417 | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) is model-agnostic and integrates with TimeChat and LLaVA-Video. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c1be86981ffdd3ff | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) reduces computation while preserving task performance. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_cad427ccdbc32788 | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) excels on queries with explicit temporal markers. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ed035893bc95a27e | 2025.emnlp-main.451 | 2,025 | Language-Guided Temporal Token Pruning (LGTTP) remains effective across general video understanding tasks. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2edace547078260f | 2024.emnlp-main.497 | 2,024 | Jellyfish models are instruction-tuned local large language models that serve as universal data preprocessing task solvers on a local, single, low-priced GPU, ensuring data security and enabling customization. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b927e038c69f2b03 | 2024.emnlp-main.497 | 2,024 | Instruction data for data preprocessing tasks is constructed using data configuration, knowledge injection, and reasoning data distillation techniques tailored to data preprocessing. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9bde7ec7979f4b3c | 2024.emnlp-main.497 | 2,024 | Jellyfish models are competitive with GPT-3.5 and GPT-4 in data preprocessing tasks. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_392e4ed8aa65cc4d | 2024.emnlp-main.497 | 2,024 | Jellyfish models exhibit strong generalizability to unseen data preprocessing tasks. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6471385a4aa779da | 2024.emnlp-main.497 | 2,024 | Jellyfish models have enhanced reasoning capabilities compared to GPT-3.5. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d047fdc8b3fe2170 | 2025.emnlp-main.840 | 2,025 | Database-Augmented Query representation (DAQu) augments the original query with query-related metadata across multiple tables. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1d4e507011076bea | 2025.emnlp-main.840 | 2,025 | Database-Augmented Query representation (DAQu) uses a graph-based set-encoding strategy to encode query-related metadata by considering hierarchies of features in the database without order. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3ec4e8c18fa51c21 | 2021.emnlp-main.834 | 2,021 | Three novel features in advanced semantics are used in text readability assessment. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f114b8b6adc8fb70 | 2021.emnlp-main.834 | 2,021 | Handcrafted features improve model performance on smaller datasets in text readability assessment. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d378bc821738238f | 2021.emnlp-main.834 | 2,021 | Hybrid models achieve state-of-the-art accuracy in text readability assessment. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_48c79cbccceb266b | 2023.emnlp-main.969 | 2,023 | KANGAROO captures the implicit graph structure among entities in closed domains. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5d70bd04ee6a309c | 2023.emnlp-main.969 | 2,023 | KANGAROO uses hyperbolic embeddings of deep hierarchical entity-class structures for knowledge fusion. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_46aa746efff8651f | 2023.emnlp-main.969 | 2,023 | KANGAROO constructs hard negative samples via contrastive learning over subgraphs. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_aa6e5af32306415e | 2023.emnlp-main.969 | 2,023 | KANGAROO outperforms existing KEPLM training paradigms on knowledge-aware and general NLP tasks in closed domains in both full and few-shot learning settings. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f9511a32882ec219 | 2025.emnlp-main.30 | 2,025 | Target-driven multi-modal alignment captures target-specific relationships in multimodal stance detection. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_40b63136b76a64ac | 2025.emnlp-main.30 | 2,025 | Dynamic weighting balances modality contributions in multimodal stance detection. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_48d6238506ec389b | 2025.emnlp-main.30 | 2,025 | T-MAD achieves robust multimodal stance detection in both in-target and zero-shot settings. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f24d5b16d1c7eb75 | 2025.emnlp-main.30 | 2,025 | Target-driven multi-modal alignment enhances multimodal stance detection effectiveness. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f1850bb883418e42 | 2025.emnlp-main.30 | 2,025 | Dynamic weighting enhances multimodal stance detection effectiveness. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d43961080f73ef22 | 2024.emnlp-main.86 | 2,024 | HiCore learns multi-level user interests by boosting multi-interest self-supervised learning with item-, entity-, and word-oriented multiple-channel hypergraphs. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f574293a7bd2b4d9 | 2023.emnlp-main.632 | 2,023 | An exhaustive overview defines the task and challenges of model editing for large language models. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_d41f380a94d625d2 | 2023.emnlp-main.632 | 2,023 | An in-depth empirical analysis evaluates the most progressive methods for editing large language models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_895262e3e5546e41 | 2023.emnlp-main.632 | 2,023 | A new benchmark dataset facilitates robust evaluation and identifies enduring issues in existing techniques for editing large language models. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_223fe981107911e6 | 2023.emnlp-main.688 | 2,023 | LEAP incorporates explicit planning into the inference procedure for multi-step logical reasoning. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_66d807b00789accd | 2023.emnlp-main.688 | 2,023 | Explicit planning makes logical reasoning decisions more informed by looking ahead into future effects. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f00459164387ba6a | 2023.emnlp-main.688 | 2,023 | LEAP uses a training strategy that safeguards explicit planning from spurious features. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_83283e28494bde46 | 2023.emnlp-main.688 | 2,023 | LEAP with small T5 models achieves competitive performance with GPT-3 on logical reasoning tasks while using significantly fewer parameters. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0924a54ff176133b | 2023.emnlp-main.688 | 2,023 | LEAP with GPT-3.5 outperforms chain-of-thought prompting on the PrOntoQA dataset. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_f3ab8d36ebaf79c9 | 2023.emnlp-main.688 | 2,023 | Explicit planning is crucial for logical reasoning performance. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ed8e5c9f873c0188 | 2021.emnlp-main.843 | 2,021 | Structurally-diverse sampling selects structurally-diverse synthetic utterance-program pairs. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3e22dbd6e2921881 | 2021.emnlp-main.843 | 2,021 | A new split of the schema2QA dataset evaluates compositional generalization in semantic parsing. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_0f0ac313f01c436d | 2021.emnlp-main.843 | 2,021 | Structurally-diverse sampling improves performance in the traditional i.i.d. setup for semantic parsing. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_966273f04ce30e7e | 2021.emnlp-main.843 | 2,021 | Structurally-diverse sampling achieves compositional generalization improvements in semantic parsing with fewer synthetic examples than uniform random sampling. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_17faa441ea9eba75 | 2025.emnlp-main.1189 | 2,025 | For English and Spanish, monolingual speech in monolingual discourse contexts is prosodically different from monolingual speech in multilingual discourse contexts. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_150a71efd03b725f | 2025.emnlp-main.1189 | 2,025 | For English and Spanish, the prosodic differences between monolingual speech in monolingual and multilingual discourse contexts are more marked with increased proximity to multilingual discourse. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3f538b8df69020f2 | 2025.emnlp-main.255 | 2,025 | MIO understands and generates speech, text, images, and videos in an end-to-end, autoregressive manner. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b3a223740d92532e | 2025.emnlp-main.255 | 2,025 | MIO is trained on a mixture of discrete tokens across four modalities using causal multimodal modeling. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a871c7b04471607c | 2025.emnlp-main.255 | 2,025 | MIO uses a four-stage training process including alignment pre-training, interleaved pre-training, speech-enhanced pre-training, and comprehensive supervised fine-tuning. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8cc2f1937b7240ce | 2025.emnlp-main.255 | 2,025 | MIO generates interleaved video-text sequences. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_a3c0133662c29fc0 | 2025.emnlp-main.255 | 2,025 | MIO performs chain-of-visual-thought reasoning. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1b029a65081bcdbd | 2025.emnlp-main.255 | 2,025 | MIO generates visual guidelines. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_44542c97df668fc2 | 2025.emnlp-main.255 | 2,025 | MIO performs instructional image editing. | 6 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b3c6e4698122138c | 2020.emnlp-main.630 | 2,020 | A new formulation for multilingual entity linking resolves language-specific mentions to a language-agnostic Knowledge Base. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c8849fb2336eb215 | 2020.emnlp-main.630 | 2,020 | An auxiliary entity-pairing task enhances dual encoder training for multilingual entity linking. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_da3c4f16f3043e78 | 2020.emnlp-main.630 | 2,020 | A single entity retrieval model covers 100+ languages and 20 million entities for multilingual entity linking. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_12202eb670695681 | 2020.emnlp-main.630 | 2,020 | A multilingual entity linking model covering 100+ languages outperforms state-of-the-art methods on a limited cross-lingual linking task. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_285de01653806cbd | 2020.emnlp-main.630 | 2,020 | Mewsli-9 is a multilingual dataset for entity linking matched to a language-agnostic Knowledge Base setting. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_72be859aaf31f21b | 2020.emnlp-main.630 | 2,020 | Frequency-based analysis provides key insights for model and training enhancements in multilingual entity linking. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_9242297bfa5261e2 | 2020.emnlp-main.626 | 2,020 | MedFilter identifies and categorizes medically relevant utterances in doctor-patient conversations. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_bd3560ea89a6779e | 2020.emnlp-main.626 | 2,020 | MedFilter integrates discourse structure and ontological knowledge to identify and categorize medically relevant utterances in doctor-patient conversations. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ff17cd309f933d04 | 2020.emnlp-main.626 | 2,020 | MedFilter improves the extraction of symptoms, medications, and complaints in medical conversations. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2309e8e0d33ac5f2 | 2025.emnlp-main.911 | 2,025 | N-Gram Trie Speculative Decoding leverages the overlap between context and model output. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_2088cdd9d37eee62 | 2025.emnlp-main.911 | 2,025 | N-Gram Trie Speculative Decoding constructs an n-gram trie from the context to generate draft tokens. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1e0b550c9a049e27 | 2025.emnlp-main.911 | 2,025 | N-Gram Trie Speculative Decoding accelerates token generation for LLMs without compromising accuracy. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_715a47613cc51aad | 2024.emnlp-main.113 | 2,024 | A novel Chinese document-level evidence retriever is developed for real-world fact-checking. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5af13dc14bec85fe | 2024.emnlp-main.113 | 2,024 | A Chinese document-level evidence retriever achieves state-of-the-art performance in evidence retrieval. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5d48627fda649f29 | 2024.emnlp-main.113 | 2,024 | An adversarial dataset is constructed for analyzing token-level biases in fact-checking systems, where each instance has high word overlap with the original but opposite veracity labels. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8063ff844f9247e9 | 2024.emnlp-main.113 | 2,024 | A language-specific fact-checking system for Chinese outperforms translation-based methods and multilingual language models in fact-checking. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_da3a8bd7834888f1 | 2024.emnlp-main.113 | 2,024 | A language-specific fact-checking system for Chinese is more robust toward token-level biases. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_eb69da9d630533a3 | 2024.emnlp-main.573 | 2,024 | Translating images to make them culturally relevant is a task in machine translation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_5b576bae9e8a0c04 | 2024.emnlp-main.573 | 2,024 | Three pipelines comprising state-of-the-art generative models perform image translation for cultural relevance. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3877054b403d8a99 | 2024.emnlp-main.573 | 2,024 | An evaluation dataset for image translation for cultural relevance has two parts: a concept part with 600 cross-culturally coherent images focusing on a single concept per image and an application part with 100 images curated from real-world applications. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_77bf424612991633 | 2024.emnlp-main.573 | 2,024 | The failure of image-editing models to translate images for cultural relevance is improved by leveraging LLMs and retrievers in the loop. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c8bf3d0f51d8eb03 | 2024.emnlp-main.573 | 2,024 | Current pipelines for image translation for cultural relevance fail for some countries in real-world applications. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_ad475d56585133df | 2022.emnlp-main.543 | 2,022 | An intent-slot co-occurrence graph captures global intent-slot correlation from the entire training corpus. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c5a934c82a514d1c | 2022.emnlp-main.543 | 2,022 | A graph neural network models the interaction between intent detection and slot filling using global intent-slot co-occurrence. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_6c3bd4f94e17308b | 2022.emnlp-main.543 | 2,022 | Using global intent-slot co-occurrence improves joint multiple intent detection and slot filling over state-of-the-art models. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_4bf05a6f9f82b041 | 2023.emnlp-main.105 | 2,023 | SCALE is a task-agnostic model that detects factual inconsistencies in long documents using a Natural Language Inference approach with large text chunks. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_1effdbdd7939a8c8 | 2023.emnlp-main.105 | 2,023 | SCALE achieves state-of-the-art performance in factual inconsistency detection for diverse tasks and long inputs. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_89009021c2316f9d | 2023.emnlp-main.105 | 2,023 | SCALE explains decisions through relevant source sentence retrieval using a novel algorithm. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_319f17f91ce72766 | 2023.emnlp-main.105 | 2,023 | ScreenEval is a long-form dialogue dataset for factual inconsistency detection. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_42db8f77bded7a56 | 2023.emnlp-main.105 | 2,023 | SCALE is more efficient than existing methods for factual inconsistency detection. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_bfa80f1eec7a3309 | 2021.emnlp-main.753 | 2,021 | Value-aware objective incorporates value vectors in attention approximation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_c429e4bf12e11b5d | 2021.emnlp-main.753 | 2,021 | In language modeling, optimal attention approximation incorporating value vectors outperforms optimal attention approximation ignoring value vectors. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_76bc47dbcd604dee | 2021.emnlp-main.753 | 2,021 | For less skewed kernel functions, value vectors have a greater impact on sparse attention approximation quality. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b8033a19f2fb3610 | 2022.emnlp-main.136 | 2,022 | ToKen decomposes the few-shot hate speech detection task into constituent parts. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b670a97c6a7a13a5 | 2022.emnlp-main.136 | 2,022 | ToKen infuses knowledge from reasoning datasets such as ATOMIC2020 for few-shot hate speech detection. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_3369bb6aaa5ecd0a | 2022.emnlp-main.136 | 2,022 | ToKen generalizes to out-of-distribution datasets in few-shot hate speech detection. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_8a27db7bd1e41633 | 2023.emnlp-main.91 | 2,023 | UL2R improves language models and their scaling curves with minimal extra compute and without new data sources. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
clm_b98f08225bd60764 | 2023.emnlp-main.91 | 2,023 | UL2R continues training a state-of-the-art large language model with UL2’s mixture-of-denoiser objective for a few additional steps. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T085601Z | 2026-05-24T08:56:02.723585+00:00 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.