Papers with temperature-controlled sampling
Are LLMs Reliable Rankers? Rank Manipulation via Two-Stage Token Optimization (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly used as rerankers, but their ranking behavior can be steered by small, natural-sounding prompts. |
| Approach: | They propose a token optimization method that crafts concise textual perturbations to consistently promote a target item in LLM-generated rankings. |
| Outcome: | The proposed method outperforms state-of-the-art base-lines and is hard to detect. |