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
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clm_fdb0d9de496390e7 | 2025.emnlp-main.14 | 2,025 | GA-DMS adaptively masks noisy textual tokens using gradient-attention similarity scores for cross-modal alignment. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_4cb53f5da8093908 | 2025.emnlp-main.14 | 2,025 | GA-DMS uses masked token prediction objectives that compel the model to predict informative text tokens, enhancing fine-grained semantic representation learning. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3c1339247a7352a9 | 2025.emnlp-main.14 | 2,025 | GA-DMS achieves state-of-the-art performance on multiple text-based person retrieval benchmarks. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_5b03f70d85e91582 | 2025.emnlp-main.597 | 2,025 | The perception bottleneck and token overload are key challenges in extending image-based vision-language models to the video domain. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_60924a3d49c529ca | 2025.emnlp-main.597 | 2,025 | D-CoDe is a training-free framework for extending image-pretrained vision-language models to video. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_cd331fda1a0e81d4 | 2025.emnlp-main.597 | 2,025 | Dynamic compression alleviates the perception bottleneck by adaptively selecting representative frames and aggregating spatial tokens in a content-aware manner. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_9f6d2448eaa402fb | 2025.emnlp-main.597 | 2,025 | Question decomposition mitigates token overload by reformulating queries into sub-questions that guide the model to focus on distinct aspects of the video. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b707f8e47dedcf24 | 2025.emnlp-main.1216 | 2,025 | Audio-Reasoner performs deep reasoning on audio. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_4d05335fb20335b8 | 2025.emnlp-main.1216 | 2,025 | CoTA is a high-quality, large-scale, multi-task dataset for audio reasoning. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_2ce54db16790d6d5 | 2025.emnlp-main.1216 | 2,025 | Structured chain-of-thought training is essential for advancing audio reasoning. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_63bfc734b904c91a | 2025.emnlp-main.837 | 2,025 | Med-PRM verifies each reasoning step against established medical knowledge bases using retrieval-augmented generation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_83c596df4488b295 | 2025.emnlp-main.837 | 2,025 | Med-PRM achieves state-of-the-art performance on medical QA benchmarks and diagnostic tasks. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b2fd29f642e0d6bc | 2025.emnlp-main.837 | 2,025 | Med-PRM integrates in a plug-and-play fashion with policy models. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_69694d12f6d94a6c | 2025.emnlp-main.837 | 2,025 | Med-PRM enables small-scale models to achieve state-of-the-art accuracy on MedQA. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_4e25a7c5519f6592 | 2025.emnlp-main.770 | 2,025 | MuLA-F uses singular value decomposition to disentangle language-specific knowledge in LoRA blocks. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_2d969eede3635408 | 2025.emnlp-main.770 | 2,025 | MuLA-F uses a local orthogonal tuning strategy. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_fb7f718dfb062371 | 2025.emnlp-main.770 | 2,025 | MuLA-F reduces multilingual knowledge conflicts in federated social media content anomaly detection. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_4681945097ca5639 | 2025.emnlp-main.770 | 2,025 | MuLA-F reduces the number of communication rounds in federated social media content anomaly detection. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_eb123ed15ab2ec9a | 2025.emnlp-main.535 | 2,025 | A taxonomy categorizes Machine Translation errors in the healthcare domain for a publicly available MT system. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_5250a78645e62a6c | 2025.emnlp-main.535 | 2,025 | An evaluation dataset for Machine Translation in healthcare for Amharic and Tigrinya is derived from pre-existing medical datasets. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_98491d0698463cf5 | 2025.emnlp-main.535 | 2,025 | Machine Translation errors in healthcare most commonly occur when the source sentence includes medical terminology and procedure descriptions, synonyms, figurative language, and word order differences. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a188cb6e6f795333 | 2025.emnlp-main.535 | 2,025 | Pre-translation interventions are not effective in reducing clinical risk when the base translation model performs poorly. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_0dbd9c95d1b9c434 | 2025.emnlp-main.319 | 2,025 | Glider uses a semantic global router that leverages LLMs' semantic reasoning to generate task-specific instructions from the input query for expert selection across all layers. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_6b57f65b693dfe95 | 2025.emnlp-main.319 | 2,025 | Glider uses a learned local router that facilitates token-level routing decisions within each module. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_cc07592d06c38d8a | 2025.emnlp-main.319 | 2,025 | Glider retrieves the correct expert for held-in tasks. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_ec072800005d1238 | 2025.emnlp-main.319 | 2,025 | Glider demonstrates compositional capabilities for held-out tasks. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_415c17f0cb9b9fe8 | 2025.emnlp-main.1263 | 2,025 | Balcony freezes the pretrained large language model and inserts additional transformer layers at selected exit points, enabling depth-based dynamic inference with minimal performance degradation. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3fdd4a0b2621aed8 | 2025.emnlp-main.1263 | 2,025 | Balcony trains the additional layers using a self-distillation loss that aligns sub-model outputs with the full model outputs. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_e3d8e1c33c1c610b | 2025.emnlp-main.1263 | 2,025 | Balcony requires fewer training tokens and tunable parameters than prior dynamic inference methods. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_dd011b9ecbe0a3b7 | 2025.emnlp-main.1263 | 2,025 | Balcony outperforms state-of-the-art dynamic inference methods and compression techniques on multiple models and benchmarks. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3e5df230062b5962 | 2025.emnlp-main.1389 | 2,025 | CogDual is a role-playing language agent that uses a cognize-then-respond reasoning paradigm. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b3ec205606183535 | 2025.emnlp-main.1389 | 2,025 | CogDual jointly models external situational awareness and internal self-awareness. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_57b867cd9c8eb093 | 2025.emnlp-main.1389 | 2,025 | CogDual generates responses with improved character consistency and contextual alignment. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f0a238c89260c40b | 2025.emnlp-main.1389 | 2,025 | CogDual uses reinforcement learning with two general-purpose reward schemes designed for open-domain text generation. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_6d0d324e03d3fa58 | 2025.emnlp-main.1389 | 2,025 | CogDual outperforms existing baselines on the CoSER, Cross-MR, and LifeChoice benchmarks. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_0e02ce390391a478 | 2025.emnlp-main.1389 | 2,025 | CogDual generalizes across diverse role-playing tasks. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_4f281a6ff879cea6 | 2025.emnlp-main.1728 | 2,025 | LiTEx is a linguistically-informed taxonomy for categorizing free-text explanations in English. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b150f2a26a4623b6 | 2025.emnlp-main.1728 | 2,025 | A subset of the e-SNLI dataset is annotated using LiTEx. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_8f16f2b9a0a13ccb | 2025.emnlp-main.1728 | 2,025 | LiTEx is reliable. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_49fd1e06e3943068 | 2025.emnlp-main.1728 | 2,025 | LiTEx aligns with Natural Language Inference labels, highlighted spans, and free-text explanations. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f655f4ac83d4e2ca | 2025.emnlp-main.1728 | 2,025 | Conditioning explanation generation on LiTEx produces explanations that are linguistically closer to human explanations than conditioning on labels or highlighted spans alone. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_2324f311c49e4a87 | 2025.emnlp-main.438 | 2,025 | VELA evaluates long image captions using an LLM-Hybrid-as-a-Judge framework. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_29737e4ef7e6ce31 | 2025.emnlp-main.438 | 2,025 | LongCap-Arena evaluates metrics for long image captions with human judgments on Descriptiveness, Relevance, and Fluency. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_d1b7c2eff51401d3 | 2025.emnlp-main.438 | 2,025 | VELA outperforms existing automatic metrics for long image caption evaluation on LongCap-Arena. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_937752c58868bd11 | 2025.emnlp-main.438 | 2,025 | VELA achieves superhuman performance on LongCap-Arena. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f9202ec711b178f6 | 2025.emnlp-main.205 | 2,025 | A multimodal event extraction strategy uses a multi-level redundant feature selection mechanism. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3c12a7c7833b3e49 | 2025.emnlp-main.205 | 2,025 | A multimodal event extraction strategy leverages knowledge editing techniques to enhance event understanding in multimodal large language models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f2c2c1524cd106dd | 2025.emnlp-main.205 | 2,025 | A multimodal event extraction strategy requires no additional parameter optimization work. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_99ab8bbc708d7f3f | 2025.emnlp-main.205 | 2,025 | A multimodal event extraction strategy that employs multi-level redundant feature selection and knowledge editing outperforms state-of-the-art baselines on the M2E2 benchmark. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_ed8f04adf12525e0 | 2025.emnlp-main.1392 | 2,025 | MrGuard uses reasoning to classify multilingual prompts for safety. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3edd8b9cecf9f8b8 | 2025.emnlp-main.1392 | 2,025 | Synthetic multilingual data generation for safety classification includes culturally and linguistically nuanced variants. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_90927d2d9a16d241 | 2025.emnlp-main.1392 | 2,025 | Group Relative Policy Optimization (GRPO) is a curriculum-based framework for multilingual safety classification. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a566076dc58cabcd | 2025.emnlp-main.1392 | 2,025 | MrGuard consistently outperforms recent baselines in multilingual safety classification across in-domain and out-of-domain languages. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_6fbfae615f1e945a | 2025.emnlp-main.1392 | 2,025 | MrGuard preserves safety judgments under multilingual variations including code-switching and low-resource language distractors. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f46dfca807589acf | 2025.emnlp-main.1392 | 2,025 | MrGuard generates explanations for safety judgments that help understand language-specific risks and ambiguities in multilingual content. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_c9677612627d98d1 | 2025.emnlp-main.472 | 2,025 | For hierarchical text classification, HYDRA treats each level in the hierarchy as a separate classification task with its own label space. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_8044a4097e166d73 | 2025.emnlp-main.472 | 2,025 | For hierarchical text classification, HYDRA uses parameter sharing and level-specific parameterization to incorporate hierarchical awareness without architectural complexity. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_31f465a067618a43 | 2025.emnlp-main.472 | 2,025 | For hierarchical text classification, HYDRA consistently outperforms flat models. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_e8ea09fd50ec112a | 2025.emnlp-main.472 | 2,025 | For hierarchical text classification, HYDRA matches or exceeds the performance of complex state-of-the-art methods. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_fc364d735b37b498 | 2025.emnlp-main.472 | 2,025 | In hierarchical text classification, complex components such as graph encoders, label semantics, and autoregressive decoders are often unnecessary. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_341fcc81651ed345 | 2025.emnlp-main.1093 | 2,025 | Inverse confidence weighting selects examples across tasks for label-efficient supervised finetuning of LLMs. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b38546bfc24318f4 | 2025.emnlp-main.1093 | 2,025 | Inverse confidence weighting is easier to implement and less computationally intensive than more complex sampling procedures for label-efficient supervised finetuning of LLMs. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_b6a1b483238857b4 | 2025.emnlp-main.1093 | 2,025 | Inverse confidence weighting achieves better accuracy than training on the complete dataset in label-efficient supervised finetuning of LLMs. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_d4232e6f62eeeafc | 2025.emnlp-main.1093 | 2,025 | Inverse confidence weighting reduces annotation costs while maintaining or improving performance compared to the best existing methods in label-efficient supervised finetuning of LLMs. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3ab804b78d812c69 | 2025.emnlp-main.345 | 2,025 | CoPriva evaluates adherence of large language models to contextual non-disclosure policies in question answering using direct and indirect attacks. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_270d899160ec0ea0 | 2025.emnlp-main.345 | 2,025 | Many large language models violate user-defined policies and leak sensitive information in question answering. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_7f1254ea1a18ab5d | 2025.emnlp-main.345 | 2,025 | Violations of user-defined policies by large language models are particularly severe against indirect attacks in question answering. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_d8626a68c6396d6c | 2025.emnlp-main.345 | 2,025 | Large language models struggle to incorporate policy constraints during generation in question answering. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_2e9ce0e8f7e0b25c | 2025.emnlp-main.345 | 2,025 | Large language models exhibit a partial ability to revise outputs when explicitly prompted in question answering. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f77f7c7862ce30df | 2025.emnlp-main.1578 | 2,025 | Waste-Bench evaluates the robustness and accuracy of VLLMs on waste classification tasks in cluttered real-world environments with complex scenes and deformed objects. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_cf2ec307c99e7f83 | 2025.emnlp-main.614 | 2,025 | Guard models are vulnerable to jailbreak attacks due to representation shifts in the embedding layer that cascade through the Transformer stack. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_2054b66db2c83c80 | 2025.emnlp-main.614 | 2,025 | Gamma-Guard uses lightweight residual adapters inserted after the embeddings and at sparse intervals in the model. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_38b70f245163144a | 2025.emnlp-main.614 | 2,025 | Gamma-Guard adapters are initialized with zero-scaled gates to retain original behavior and then fine-tuned adversarially to denoise embeddings and refocus attention. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_3e004cd5a322ebf8 | 2025.emnlp-main.614 | 2,025 | Gamma-Guard achieves high accuracy on jailbreak attack examples with minimal degradation in accuracy on clean examples. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_23363f34f235e904 | 2025.emnlp-main.614 | 2,025 | Gamma-Guard's robustness improvements persist across different layer placements and model sizes. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_98e98cc13f7a9e3d | 2025.emnlp-main.240 | 2,025 | QSPEC decouples efficiency from quality by integrating low-precision joint quantization for fast drafting and high-precision weight-only quantization for accurate verification via speculative decoding. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_5619a5f9b8824503 | 2025.emnlp-main.240 | 2,025 | QSPEC reuses weights and KV cache across stages to enable near-zero-cost switching without retraining or auxiliary models. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a95be5449a372ace | 2025.emnlp-main.240 | 2,025 | QSPEC achieves speedup without quality degradation compared to high-precision baselines. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_62340f0f117292f2 | 2025.emnlp-main.240 | 2,025 | QSPEC outperforms state-of-the-art speculative decoding methods in batched settings. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a6a2fa4d38a6db6e | 2025.emnlp-main.240 | 2,025 | QSPEC supports plug-and-play deployment. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a14173f8a7ed5437 | 2025.emnlp-main.240 | 2,025 | QSPEC generalizes across model scales, quantization methods, and workloads. | 5 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_c8303a1f4bbf3b95 | 2025.emnlp-main.1110 | 2,025 | ArgCMV is a key point extraction dataset for long-context online discussions from actual human debates. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_1926d2d2b3ceade4 | 2025.emnlp-main.1110 | 2,025 | ArgCMV exhibits higher complexity than ArgKP21 for key point extraction. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_956b9d8d507320e2 | 2025.emnlp-main.1110 | 2,025 | Existing key point extraction methods do not adapt well to ArgCMV. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f863ab6c94d553a6 | 2025.emnlp-main.1110 | 2,025 | ArgCMV is curated using state-of-the-art large language models. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_32750422af9cb72c | 2025.emnlp-main.760 | 2,025 | The MUCAR multilingual dataset contains visual contexts that uniquely resolve ambiguous textual expressions. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_a3a5a2dad7cc4a21 | 2025.emnlp-main.760 | 2,025 | The MUCAR dual-ambiguity dataset contains pairs of ambiguous images and ambiguous textual contexts that produce a single clear interpretation through mutual disambiguation. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_7c8e3ce17d47c839 | 2025.emnlp-main.760 | 2,025 | Evaluations of multimodal models on MUCAR reveal substantial performance gaps compared to human-level performance. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f9f901895779a49a | 2025.emnlp-main.918 | 2,025 | Within a hierarchical text classification task, different prompting strategies yield substantially different results from large language models. | 0 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_98b677aa32c9cb2e | 2025.emnlp-main.918 | 2,025 | Across different hierarchical text classification tasks, the relative effectiveness of a prompting strategy varies significantly. | 1 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_f3984ac6a3d5b334 | 2025.emnlp-main.918 | 2,025 | HiEPS integrates the results of diverse hierarchical text classification prompting strategies for zero-shot hierarchical text classification. | 2 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_8a24ddc58c79561f | 2025.emnlp-main.918 | 2,025 | The path-valid voting mechanism selects a valid hierarchical text classification result with the highest path frequency score. | 3 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
clm_5db0fafc530e6d0f | 2025.emnlp-main.918 | 2,025 | HiEPS outperforms single prompting strategies for zero-shot hierarchical text classification. | 4 | qwen/qwen3-235b-a22b-thinking-2507 | f9a9b434c72c | extract_20260524T094754Z | 2026-05-24T09:47:55.807293+00:00 |
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