Papers with search strategy
Unveiling the Lexical Sensitivity of LLMs: Combinatorial Optimization for Prompt Enhancement (2024.emnlp-main)
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| Challenge: | Large language models (LLMs) demonstrate exceptional instruct-following ability to complete downstream tasks. |
| Approach: | They propose a black-box combinatorial optimization framework that iteratively improves lexical choices in prompts by a search strategy related to word influence. |
| Outcome: | The proposed framework recovers the model's ability to instruct-follow and solve downstream tasks even when the variations are imperceptible to humans. |
QDTSynth: Quality-Driven Formal Theorem Synthesis for Enhancing Proving Performance of LLMs (2025.acl-long)
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| Challenge: | Existing formal languages such as Lean, Coq and Metamath are proving to be useful in formal theorem proving . however, there is a scarcity of high-quality supervised fine-tuning data for formal proofs . |
| Approach: | They propose a Q**uality-**D**riven **T**heorem **S**ynthesis method in Lean4 . they propose diversity screening and the self-assessment method to select theoremas that exhibit diversity and high quality from the initial synthetic statements. |
| Outcome: | The proposed method significantly improves performance of open-source LLMs in theorem proving tasks. |
UnitY: Two-pass Direct Speech-to-speech Translation with Discrete Units (2023.acl-long)
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Hirofumi Inaguma, Sravya Popuri, Ilia Kulikov, Peng-Jen Chen, Changhan Wang, Yu-An Chung, Yun Tang, Ann Lee, Shinji Watanabe, Juan Pino
| Challenge: | Experimental evaluations show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. |
| Approach: | They propose a two-pass direct S2ST architecture which generates textual representations and predicts discrete acoustic units . they show that UnitY outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up. |
| Outcome: | The proposed architecture outperforms a single-pass speech-to-unit translation model by 2.5-4.2 ASR-BLEU with 2.83x decoding speed-up on large datasets. |