The paper investigates how language models avoid overgeneralizations by distinguishing between two types of indirect negative evidence: preemption and entrenchment. Through controlled rearing experiments on models trained on child‑caregiver conversations, the authors find that models do not exhibit verb‑specific preemption but show weak abstract preemption. Analysis of training dynamics suggests that competing structures act as indirect positive evidence rather than negative in the verb‑specific condition.
By Yixuan Wang, Freda Shi, Kanishka Misra
CLUE is a two‑stage training pipeline that teaches models to discover and use social context cues within a conversation for pragmatic understanding. It combines supervised evidence reasoning with a GRPO reward that credits new, evidence‑supported social inferences while penalizing excessive reasoning effort. Experiments show that CLUE improves weighted F1 scores by 17.3% in‑domain and 9.3% out‑of‑domain, reduces reasoning tokens by over 80%, and aligns its explanations more closely with human‑selected conversational evidence.
By Chengfei Wu, Zhaoqing Wu, Dan Goldwasser
arXiv:2606. 00230v1 Announce Type: new Abstract: Grokking, the phenomenon in which neural networks generalize long after fitting their training data, has been studied in supervised settings on many epochs.
By Sherin Muckatira, Namrata Shivagunde, Vijeta Deshpande, Anna Rumshisky
arXiv:2608.22887v1 Announce Type: new
Abstract: Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy...
By Zengqing Wu, Chuan Xiao
arXiv:2602. 02712v2 Announce Type: replace Abstract: A popular approach to post-training control of large language models (LLMs) is the steering of intermediate latent representations.
By Magamed Taimeskhanov, Samuel Vaiter, Damien Garreau
Large language models (LLMs) are entering decisions in triage and lending, where task-relevant inference must be distinguished from impermissible proxy use. Current audits ask whether decisions change...
Coordination, a fundamental linguistic structure, remains a subject of intense debate, and its exact nature continues to elude theoretical linguistics. A common view holds that only same-category constituents can be conjoined, which has been challenged by the many grammatical unlike coordinations found in natural language.
The study evaluates 12 instruction‑tuned open‑weight LLMs on six causal‑graph benchmarks, testing five prompting strategies and four confidence sources. Findings show that LLMs tend to over‑predict edges, misclassify indirect or reversed edges as direct, and exhibit high over‑confidence, while conventional confidence estimates are unreliable and agreement signals offer limited improvement. The results suggest LLMs should be used as externally validated soft causal priors rather than definitive causal‑structure evidence.
By Amit Kumar, Elnur Adl Zarabi, Suranjana Trivedy, Zhiqian Chen, Lei Zhang, Kaiqun Fu, Taoran Ji
The paper investigates how large language models acquire knowledge during pre‑training, proposing that auxiliary views—reformulations of knowledge—are causally beneficial. Experiments show that repetition is essential, paraphrasing helps only at smaller batch sizes, and reallocating tokens from repetition to auxiliary views improves learning even for factual recall. The study also finds that the benefit of auxiliary views does not depend on the teacher model’s strength, identifies specific knowledge types that aid learning, and explores mechanistic effects via layer‑wise biases and compression.
By Joseph Lee, Yidi Huang, Dokyoon Kim, Shu Yang, Li Shen
The study investigates how language models acquire in-context learning (ICL) by distinguishing between copying a nearby demonstration and integrating information across a prompt. Across 17 models, roughly one-third default to copied answers, a few are undecided, and the remainder favor rule-consistent answers that yield higher accuracy and are guided by the requested value rather than the nearest conflicting example. Analyses show that models prioritize task-relevant prompt tokens before accuracy surpasses chance, and that synthetic or fine-tuned training can shift models toward compositional responses or improve accuracy when prompts reassign label meanings.
By Minglai Yang, Xinyu Guo, Renxiong Wang, Sunjie Hou, Daniel Yue Zhang, Yunzhong He
arXiv:2608. 16627v1 Announce Type: cross Abstract: Natural language explanations (NLEs) are increasingly used as inputs, for example, as few-shot rationales that influence model behavior in in-context learning (ICL).
By Mahdi Dhaini, Adam Dejl, Juraj Vladika, Volkan \"Ozer, Barbara Plank, Gjergji Kasneci
arXiv:2609.24238v1 Announce Type: new
Abstract: We reproduce and stress-test the work of Yu et al. (2023), who characterize how language models (LMs) arbitrate between memorized knowledge and contrad...
By Guilhem Fouilh\'e, Nicholas Asher, Philippe Muller