LLM popularity judgments align more closely with pretraining data exposure counts than with Wikipedia popularity, with stronger effects in pairwise comparisons and larger models.
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Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing , year =
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Factual associations in autoregressive transformers are localized to mid-layer feed-forward modules and can be edited via rank-one model editing while preserving both specificity and generalization on counterfactual tests.
TrustMargin arbitrates between direct and RAG answers from a frozen LLM by combining a parametric-prior margin and an evidence-binding margin computed from model likelihoods, improving results on 2WikiMQA and CWQA.
Indirect elicitation via triplet comparisons recovers meaningful association structures from LLMs and supports conservative causal candidate links across prompted subpopulations.
Norm-Anchor Scaling breaks the norm-feedback loop in sequential LLM editing by anchoring value vectors to original norms, improving long-run performance by 72.2% and extending the editing horizon over 4x.
OPT releases open decoder-only transformers up to 175B parameters that match GPT-3 performance at one-seventh the carbon cost, along with code and training logs.
Proposes a textbook-based true/false QA task where PTLMs score ~50% closed-book even after pre-training on the text and ~60% open-book with retrieval.
Cross-lingual prompt exploration improves factual recall and consistency in LLMs across 17 languages more efficiently than native-language scaling.
Empirical study of LLM brand recommendations across industries finds moderate concentration (mean Gini 0.28) and low cross-model agreement (41.6%) on top brands.
Controlled experiments show implicit multi-hop reasoning in LLMs requires prior exposure to compositional contexts during pretraining and does not transfer to unexposed individuals.
LLMs encode accurate but brittle internal beliefs about latent game states and convert them poorly into actions, creating systematic gaps that explain strategic failures.
R³AG routes queries to retrievers by decomposing capabilities into retrieval quality and generation utility, trained via contrastive learning on document assessments and downstream answer correctness to outperform static methods.
LLM-Metrics probes memory in 17 LLMs across 549 2023-2024 CS papers and finds a modest Spearman correlation (rho=0.1495) with citation counts, stronger for 2024 papers.
QREAM rewrites documents to question-focused style using iterative ICL and distilled FT models, boosting RAG performance by up to 8% relative improvement.
KLCF formalizes long-form factuality as bidirectional distribution matching between expressed and parametric knowledge, using a sampled factual checklist for recall and a truthfulness reward for precision.
The paper surveys hallucination in LLMs with an innovative taxonomy, factors, detection methods, benchmarks, mitigation strategies, and open research directions.
The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.
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LLM-Metrics: Measuring Research Impact Through Large Language Model Memory
LLM-Metrics probes memory in 17 LLMs across 549 2023-2024 CS papers and finds a modest Spearman correlation (rho=0.1495) with citation counts, stronger for 2024 papers.