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VALTEST: Automated Validation of Language Model Generated Test Cases
Large Language Models (LLMs) have demonstrated significant potential in automating
software testing, specifically in generating unit test cases. However, the validation of LLM …
software testing, specifically in generating unit test cases. However, the validation of LLM …
Representation Engineering for Large-Language Models: Survey and Research Challenges
Large-language models are capable of completing a variety of tasks, but remain
unpredictable and intractable. Representation engineering seeks to resolve this problem …
unpredictable and intractable. Representation engineering seeks to resolve this problem …
Think More, Hallucinate Less: Mitigating Hallucinations via Dual Process of Fast and Slow Thinking
Large language models (LLMs) demonstrate exceptional capabilities, yet still face the
hallucination issue. Typical text generation approaches adopt an auto-regressive generation …
hallucination issue. Typical text generation approaches adopt an auto-regressive generation …
HalluCana: Fixing LLM Hallucination with A Canary Lookahead
In this paper, we present HalluCana, a canary lookahead to detect and correct factuality
hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana …
hallucinations of Large Language Models (LLMs) in long-form generation. HalluCana …
VideoICL: Confidence-based Iterative In-context Learning for Out-of-Distribution Video Understanding
Recent advancements in video large multimodal models (LMMs) have significantly improved
their video understanding and reasoning capabilities. However, their performance drops on …
their video understanding and reasoning capabilities. However, their performance drops on …
CoCo-CoLa: Evaluating Language Adherence in Multilingual LLMs
Multilingual Large Language Models (LLMs) develop cross-lingual abilities despite being
trained on limited parallel data. However, they often struggle to generate responses in the …
trained on limited parallel data. However, they often struggle to generate responses in the …
Attention-guided Self-reflection for Zero-shot Hallucination Detection in Large Language Models
Q Liu, X Chen, Y Ding, S Xu, S Wu, L Wang - arxiv preprint arxiv …, 2025 - arxiv.org
Hallucination has emerged as a significant barrier to the effective application of Large
Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf …
Language Models (LLMs). In this work, we introduce a novel Attention-Guided SElf …
CoT-UQ: Improving Response-wise Uncertainty Quantification in LLMs with Chain-of-Thought
B Zhang, R Zhang - arxiv preprint arxiv:2502.17214, 2025 - arxiv.org
Large language models (LLMs) excel in many tasks but struggle to accurately quantify
uncertainty in their generated responses. This limitation makes it challenging to detect …
uncertainty in their generated responses. This limitation makes it challenging to detect …
Sample, Scrutinize and Scale: Effective Inference-Time Search by Scaling Verification
Sampling-based search, a simple paradigm for utilizing test-time compute, involves
generating multiple candidate responses and selecting the best one--typically by verifying …
generating multiple candidate responses and selecting the best one--typically by verifying …
Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to Minimize User Input
Existing embodied instance goal navigation tasks, driven by natural language, assume
human users to provide complete and nuanced instance descriptions prior to the navigation …
human users to provide complete and nuanced instance descriptions prior to the navigation …