Large Language Models Are Bad Dice Players: LLMs Struggle to Generate Random Numbers from Statistical Distributions (2026.acl-long)
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| Challenge: | Existing large language models lack a functional internal sampler to faithfully sample from specified probability distributions . lack of robust sampling mechanisms across diverse application scenarios is a critical functional requirement . |
| Approach: | They propose to use a dual-protocol design to disentangle failure modes . batch generation achieves only modest statistical validity, while independent requests collapse almost entirely . |
| Outcome: | The proposed model fails to enforce uniform answer-position constraints and violates demographic targets in attribute-constrained text-to-image prompt synthesis. |
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| Challenge: | LLMs are used to emulate sequential decision-making processes of humans . however, their ability to perform probabilistic sampling is limited . |
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A Survey of Confidence Estimation and Calibration in Large Language Models (2024.naacl-long)
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| Challenge: | Large language models (LLMs) have demonstrated impressive capabilities across a wide range of tasks in various domains, but they can be unreliable due to factual errors in their generations. |
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Large Language Models Are Still Misled by Simple Bias Ensembles (2026.findings-acl)
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| Challenge: | Existing benchmarks for large language models are constrained to datasets where each sample is manually injected with only one type of bias. |
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| Challenge: | Current approaches to evaluation of large language models ignore high entropy of LLM responses. |
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On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey (2024.findings-acl)
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| Challenge: | Large Language Models (LLMs) provide a data-centric solution to alleviate limitations of real-world data with synthetic data generation. |
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Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation (2026.acl-long)
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Qianli Wang, Van Bach Nguyen, Yihong Liu, Fedor Splitt, Nils Feldhus, Christin Seifert, Hinrich Schuetze, Sebastian Möller, Vera Schmitt
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A Systematic Survey and Critical Review on Evaluating Large Language Models: Challenges, Limitations, and Recommendations (2024.emnlp-main)
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Md Tahmid Rahman Laskar, Sawsan Alqahtani, M Saiful Bari, Mizanur Rahman, Mohammad Abdullah Matin Khan, Haidar Khan, Israt Jahan, Amran Bhuiyan, Chee Wei Tan, Md Rizwan Parvez, Enamul Hoque, Shafiq Joty, Jimmy Huang
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The Data Frontier for Large Language Models: Selection, Synthesis, and Tools (2026.acl-tutorials)
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| Challenge: | acquiring and curating high-quality training data remains a significant bottleneck . acquiring such high-quality data is a key challenge for researchers and practitioners . |
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When Life Gives You Samples: The Benefits of Scaling up Inference Compute for Multilingual LLMs (2025.emnlp-main)
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| Challenge: | Recent advances in large language models have shifted focus toward scaling inference-time compute. |
| Approach: | They propose to scale inference-time compute in a multilingual, multi-task setting . they propose to use m-ArenaHard-v2.0 prompts to sample multiple outputs in parallel . |
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Exploring the Hidden Capacity of LLMs for One-Step Text Generation (2025.emnlp-main)
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| Challenge: | Large language models (LLMs) can reconstruct surprisingly long texts via autoregressive generation from just one trained input embedding. |
| Approach: | They show that large language models can reconstruct surprisingly long texts via autoregressive generation from just one trained input embedding. |
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