Papers with Reranking
ProRank: Prompt Warmup via Reinforcement Learning for Small Language Models Reranking (2026.findings-acl)
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| Challenge: | Recent Large Language Models (LLMs) have demonstrated remarkable performance in document reranking tasks. |
| Approach: | They propose a two-stage training approach for document reranking using reinforcement learning and fine-grained score learning. |
| Outcome: | The proposed approach outperforms open-source and proprietary reranking models on BEIR benchmark. |
A Bayesian Optimization Approach to Machine Translation Reranking (2025.naacl-long)
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| Challenge: | reranking is a method of improving prediction quality but can add computational cost. |
| Approach: | They propose to score a list of prediction candidates with an external scoring model and return the highest-scoring candidate. |
| Outcome: | The proposed method achieves the same CometKiwi score using 70 evaluations on average compared to scoring a subset of 180 candidates. |
Lightweight reranking for language model generations (2024.acl-long)
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| Challenge: | Large Language Models (LLMs) can exhibit considerable variation in quality of sampled outputs. |
| Approach: | They propose a method for reranking LLM generations using pairwise statistics . they show strong improvements for selecting the best k generations for code generation tasks . |
| Outcome: | The proposed approach improves selection and generation quality for code generation tasks and autoformalization, summarization, and translation tasks. |
Tracing and Dissecting How LLMs Recall Factual Knowledge for Real World Questions (2025.acl-long)
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| Challenge: | Recent advances in large language models have shown promising ability to perform commonsense reasoning. |
| Approach: | They propose a two-dimensional analysis framework that incorporates token back-tracing and token decoding to uncover how LLMs conduct factual knowledge recall. |
| Outcome: | The proposed framework shows that LLMs lack relevant knowledge but struggle to select the most accurate information based on context during the retrieval and rerank phase. |