Papers with re-ranking mechanism
TRACE: An Experiential Framework for Coherent Multi-hop Knowledge Graph Question Answering (2026.findings-acl)
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| Challenge: | Existing methods for multihop Knowledge Graph Question Answering (KGQA) treat each reasoning step independently and fail to leverage experience from prior explorations, leading to fragmented reasoning and redundant exploration. |
| Approach: | They propose a framework that unifies LLM-driven contextual reasoning with exploration prior integration to enhance coherence and robustness of multihop KGQA. |
| Outcome: | Extensive experiments on multiple KGQA benchmarks show that TRACE outperforms state-of-the-art methods. |
Definition Modelling for Appropriate Specificity (2021.emnlp-main)
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| Challenge: | Existing definition generation techniques have faced various problems such as the out-of-vocabulary problem and over/under-specificity problems. |
| Approach: | They propose to leverage a pre-trained encoder-decoder model and introduce a re-ranking mechanism to model specificity in definitions. |
| Outcome: | The proposed method significantly outperforms the state-of-the-art method on standard evaluation datasets and shows that it addresses the over/under-specificity problems. |
Ranking LLM-Generated Loop Invariants for Program Verification (2023.findings-emnlp)
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Saikat Chakraborty, Shuvendu Lahiri, Sarah Fakhoury, Akash Lal, Madanlal Musuvathi, Aseem Rastogi, Aditya Senthilnathan, Rahul Sharma, Nikhil Swamy
| Challenge: | Large Language Models (LLMs) are capable of synthesizing inductive loop invariants for a class of programs in a 0-shot setting, yet require several samples to generate the correct invariant. |
| Approach: | They propose a re-ranking approach to generate inductive loop invariants using Large Language Models . they propose reranking rankers that can distinguish between correct and incorrect attempts . |
| Outcome: | The proposed method reduces the number of calls to a verifier by comparing the generated results with the original model. |