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最近一次命中来自 LM：MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
2026-06-15
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命中明细
按日期回看匹配到这个关键词的论文标题，并保留来源 feed 信息。
2026-06-25
MedGuards: Multi-Agent System for Reliable Medical Error Detection and Correction
As Large Language Models (LLMs) are increasingly deployed in healthcare settings, accurate error detection and correction in generated or existing text becomes critical, as even m…
2026-06-24
Pigeonholing: Bad prompts hurt models to collapse and make mistakes
While in-context learning is generally shown to be effective in Large Language Models (LLMs), bad contexts can cause performance degradation and mode collapse, a phenomenon we cal…
2026-06-19
Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
Large language models (LLMs) have achieved strong performance across a wide range of language-based tasks by leveraging both extensive parametric knowledge and in-context learning…
What Do Safety-Aligned LLMs Learn From Mixed Compliance Demonstrations?
Prior work has shown that in-context demonstrations can jailbreak language models, but it remains unclear how models interpret different types of compliance demonstrations. We stu…
2026-06-17
Querying an astronomical database using large language models: the ALeRCE text-to-SQL system
We develop a text-to-SQL (structured query language) system based on large language models (LLMs) using in-context learning and apply it to the Automatic Learning for the Rapid Cl…
2026-06-04
Caliper: Probing Lexical Anchors versus Causal Structure in LLMs
Large language models reach 50 to 70% accuracy on causal reasoning benchmarks such as CLadder, but it is unclear whether this reflects structural reasoning or lexical pattern matc…
2026-06-03
Reasoning over Grammar: Can Synthetic Linguistic Reasoning Traces Enhance Low-Resource Machine Translation?
Large language models (LLMs) offer a promising approach to machine translation (MT) for extremely low-resource languages by incorporating linguistic resources through in-context l…
2026-05-08
BioTool: A Comprehensive Tool-Calling Dataset for Enhancing Biomedical Capabilities of Large Language Models
Despite the success of large language models (LLMs) on general-purpose tasks, their performance in highly specialized domains such as biomedicine remains unsatisfactory. A key lim…
2026-04-17
From Image to Pixels: towards Fine-Grained Medical Vision-Language Models.
Multimodal large language models (MLLMs) offer immense potential for biomedical AI, yet current applications remain limited to coarse-grained image understanding and basic textual…