Prefix gain measured via student-model solve-rate improvement is used to train a Prefix Utility Model (PUM) that supplies stronger supervision than correctness-based process rewards for mathematical reasoning.
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Transactions of the Association for Computational Linguistics 10 (Jan
18 Pith papers cite this work. Polarity classification is still indexing.
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A panel of smaller diverse LLMs outperforms a single large model as an evaluator of generations, showing less intra-model bias and over 7x lower cost.
MultiHop-RAG is a new benchmark dataset demonstrating that existing retrieval-augmented generation systems perform poorly on multi-hop queries requiring retrieval and reasoning over multiple evidence pieces.
Empirical audit of LAION-2B-en and LAION-2B-multi finds overrepresentation of young adults, White people, and males plus stereotypical emotion associations across two attribute classifiers.
Manual audit shows web-scraped Lombard corpora are largely noisy and biased toward Western varieties over Eastern ones.
Introduces CHALIS benchmark dataset testing language ID on mutually intelligible cousin language pairs and orthographically noisy inputs, with evaluation showing existing systems struggle substantially.
Decisive combines document-grounded option scoring with adaptive Bayesian preference elicitation to achieve up to 20% higher decision accuracy than LLMs and existing frameworks across domains.
Introduces a parameter-driven framework for data attribution in LLMs that enables negotiation among creators, users, and intermediaries to meet stakeholder goals within the data economy.
The study filters non-English Wikipedia, reveals quality problems, proposes a 4-level ranking, and shows filtered data matches or beats raw data in language modeling with largest gains for lower-quality editions.
Contrastive Activation Addition steers Llama 2 Chat by adding averaged residual-stream activation differences from contrastive example pairs to control targeted behaviors at inference time.
GPT-NeoX-20B is a publicly released 20B parameter autoregressive language model trained on the Pile that shows strong gains in five-shot reasoning over similarly sized prior models.
ProbScale finds layer subsets in SLMs like RoBERTa-Large and T5-Base that cut parameters 5-10x while retaining 95-98% of original task performance by maximizing aggregated probe scores under a budget.
Analysis estimates 18.7% of Common Crawl documents contain geospatial information like coordinates and addresses, with little difference by language.
LLM-extracted patterns merging logical structures and linguistic cues yield statistically significant gains in fallacy classification over zero-shot baselines with cross-dataset generalization.
Large-scale benchmarks of multilingual embeddings and QE models show no universal performer; direction-aware routing and calibration recommended for parallel data assessment.
MMoA adds LSTM recurrence to Mixture-of-Agents routing, reaching 58.0% win rate on AlpacaEval 2.0 versus 59.8% for baseline MoA while cutting runtime by up to 4.6%.
Fine-tuned PEGASUS achieves state-of-the-art ROUGE scores on XL-Sum English corpus with 4.04% ROUGE-1, 15.25% ROUGE-2, and 3.39% ROUGE-L gains over mT5 baseline.
A survey that catalogs threat models, detection approaches, and mitigation strategies for toxicity in multilingual LLMs while identifying challenges such as uneven language coverage and culturally variable harm definitions.
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A Human-Centric Framework for Data Attribution in Large Language Models
Introduces a parameter-driven framework for data attribution in LLMs that enables negotiation among creators, users, and intermediaries to meet stakeholder goals within the data economy.