Tool-Adaptive LLM Reranker
Generative Large Language Models (LLMs) have revolutionized information retrieval, yet their strictly parametric nature frequently leads to severe factual hallucinations when confronted with complex queries beyond their epistemic boundaries. While external tool-calling can mitigate this, indiscriminately invoking search tools for every document during reranking incurs prohibitive latency overheads, creating an intractable accuracy-efficiency dilemma. To address this challenge, we propose TALRanker, a novel framework that formalizes pointwise relevance scoring as an agentic Markov decision process. We optimize it via a two-stage training paradigm. An initial warm-up utilizes a language-pre...
| Authors | Zichuan Liu, Ruijin Hua |
|---|---|
| Primary category | cs.IR |
| Published | 12 July 2026 |
| Last updated at source | 12 July 2026 |
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