Papers with acceptance rates
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (2021.emnlp-main)
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| Challenge: | EMNLP 2021 is one of the first hybrid conferences in the field of natural language processing. |
| Approach: | EMNLP 2021 is one of the first hybrid conferences in the field of natural language processing. |
| Outcome: | EMNLP 2021 is one of the first hybrid conferences in the field of natural language processing. |
Do Large Language Models Discriminate in Hiring Decisions on the Basis of Race, Ethnicity, and Gender? (2024.acl-short)
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| Challenge: | We study whether large language models exhibit race- and gender-based name discrimination in hiring decisions . |
| Approach: | They propose templatic prompts to LLMs to write an email to a named job applicant informing them of a hiring decision. |
| Outcome: | The proposed model generates an acceptance or rejection email based on the applicant's first name . |
Speculative Decoding for Multi-Sample Inference (2025.findings-emnlp)
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Yiwei Li, Jiayi Shi, Shaoxiong Feng, Peiwen Yuan, Xinglin Wang, Yueqi Zhang, Ji Zhang, Chuyi Tan, Boyuan Pan, Yao Hu, Kan Li
| Challenge: | Speculative decoding method exploits consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases. |
| Approach: | They propose a speculative decoding method that exploits the consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or external databases. |
| Outcome: | The proposed method exploits the intrinsic consensus of parallel reasoning paths to synthesize high-quality draft tokens without auxiliary models or databases. |
Speculative Streaming: Efficient and Scalable Speculative Decoding with Multi-Stream Attention (2025.emnlp-main)
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Nikhil Bhendawade, Irina Belousova, Qichen Fu, Henry Mason, Antonie Lin, Mohammad Rastegari, Mahyar Najibi
| Challenge: | Speculative decoding is a prominent technique for accelerating LLM inference by leveraging an auxiliary draft model, but its effectiveness is limited by the autoregressive nature of draft generation. |
| Approach: | They propose a method that integrates speculative draft generation directly within the target model using multi-stream attention. |
| Outcome: | The proposed method improves acceptance but also latency and speculation latency, limiting overall speedup. |
DReSD: Dense Retrieval for Speculative Decoding (2025.findings-acl)
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| Challenge: | Speculative decoding (SD) uses an efficient draft model to propose the next few tokens, which are verified by the LLM in a single forward call, reducing latency while preserving its outputs. |
| Approach: | They propose a draft model that proposes the next few tokens from a non-parametric datastore and uses a framework that uses approximate nearest neighbour search with contextualised token embeddings to retrieve the most semantically relevant sequences for SD. |
| Outcome: | The proposed framework achieves (on average) 87% higher acceptance rates, 65% longer accepted tokens and 19% faster generation speeds compared to sparse retrieval (REST). |
SpecHub: Provable Acceleration to Multi-Draft Speculative Decoding (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have limited inference speed due to sequential token generation . Spechub is a novel, efficient sampling-verification method for MDSD that improves acceptance rates with only linear computational overhead. |
| Approach: | They propose a method that uses a smaller draft model to generate multiple token sequences . Spechub generates 0.05-0.27 and 0.02-0.16 more tokens per step than RRS and RRS without replacement . |
| Outcome: | The proposed method improves acceptance rates with only linear computational overhead. |
Multimodal Safety Evaluation in Generative Agent Social Simulations (2026.acl-long)
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Alhim Adonai Vera Gonzalez, Carlos Hinojosa, Karen Sanchez, Haidar Bin Hamid, Donghoon Kim, Bernard Ghanem
| Challenge: | Recent advances in large language models have enabled generative agents that simulate be-like behavior through natural language interactions. |
| Approach: | They propose a reproducible simulation framework to evaluate generative agents in multimodal scenarios . they use metrics that quantify plan revisions and unsafe-to-safe conversions to evaluate their effectiveness . |
| Outcome: | The proposed framework evaluates generative agents in three aspects: safety improvement over time, detection of unsafe activities across social contexts, social dynamics and acceptance rates. |
GUIDE: Towards Scalable Advising for Research Ideas (2026.acl-long)
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| Challenge: | Existing systems that provide detailed, constructive feedback on academic papers struggle with review fidelity. |
| Approach: | They explore factors that underlie the development of robust advising systems . large language models have shown remarkable progress in tasks from text generation to code synthesis . |
| Outcome: | The proposed model outperforms general-purpose language models in acceptance rates for self-ranked top-30% submissions to ICLR 2025. |