The widespread success of large language models (LLMs) on NLP benchmarks has been accompanied by concerns that LLMs function primarily as stochastic parrots that reproduce texts similar to what they saw during pre-training, often erroneously.But what is the nature of their errors, and do these errors exhibit any regularities?In this work, we examine irrelevant context hallucinations, in which models integrate misleading contextual cues into their predictions.Through behavioral analysis, we show that these errors result from a structured yet flawed mechanism that we term class-based (mis)generalization, in which models combine abstract class cues with features extracted from the query or context to derive answers.Furthermore, mechanistic interpretability experiments on Llama-3, Mistral, and Pythia across 39 factual recall relation types reveal that this behavior is reflected in the model's internal computations: (i) abstract class representations are constructed in lower layers before being refined into specific answers in higher layers, (ii) feature selection is governed by two competing circuits -one prioritizing direct query-based reasoning, the other incorporating contextual cues -whose relative influences determine the final output.Our findings provide a more nuanced perspective on the stochastic parrot argument: through form-based training, LLMs can exhibit generalization leveraging abstractions, albeit in unreliable ways based on contextual cues -what we term stochastic chameleons. 1 * Equal contribution.

Final-answer-based metrics are commonly used for evaluating large language models (LLMs) on math word problems, often taken as proxies for reasoning ability. However, such metrics conflate two distinct sub-skills: abstract formulation (capturing mathematical relationships using expressions) and arithmetic computation (executing the calculations). Through a disentangled evaluation on GSM8K and SVAMP, we find that the final-answer accuracy of Llama-3 and Qwen2.5 (1B-32B) without CoT is overwhelmingly bottlenecked by the arithmetic computation step and not by the abstract formulation step. Contrary to the common belief, we show that CoT primarily aids in computation, with limited impact on abstract formulation. Mechanistically, we show that these two skills are composed conjunctively even in a single forward pass without any reasoning steps via an abstract-then-compute mechanism: models first capture problem abstractions, then handle computation. Causal patching confirms these abstractions are present, transferable, composable, and precede computation. These behavioural and mechanistic findings highlight the need for disentangled evaluation to accurately assess LLM reasoning and to guide future improvements.

While large language models (LLMs) have achieved impressive performance in generating fluent and realistic text, controlling the generated text so that it exhibits properties such as safety, factuality, and non-toxicity remains challenging. Existing decoding-based controllable text generation methods are static in terms of the dimension of control; if the target subject is changed, they require new training. Moreover, it can quickly become prohibitive to concurrently control multiple subjects. To address these challenges, we first show that existing methods can be framed as a reinforcement learning problem, where an action-value function estimates the likelihood of a desired attribute appearing in the generated text. Then, we introduce a novel approach named SF-Gen, which leverages the concept of successor features to decouple the dynamics of LLMs from task-specific rewards. By employing successor features, our method proves to be memory-efficient and computationally efficient for both training and decoding, especially when dealing with multiple target subjects. To the best of our knowledge, our research represents the first application of successor features in text generation. In addition to its computational efficiency, the resultant language produced by our method is comparable to the SOTA (and outperforms baselines) in both control measures as well as language quality, which we demonstrate through a series of experiments in various controllable text generation tasks.

State-of-the-art abstractive summarization systems often generate hallucinations; i.e., content that is not directly inferable from the source text.Despite being assumed incorrect, we find that much hallucinated content is factual, namely consistent with world knowledge.These factual hallucinations can be beneficial in a summary by providing useful background information.In this work, we propose a novel detection approach that separates factual from non-factual hallucinations of entities.Our method utilizes an entity's prior and posterior probabilities according to pre-trained and finetuned masked language models, respectively.Empirical results suggest that our approach outperforms five baselines and strongly correlates with human judgments.Furthermore, we show that our detector, when used as a reward signal in an off-line reinforcement learning (RL) algorithm, significantly improves the factuality of summaries while maintaining the level of abstractiveness. 1

State-of-the-art abstractive summarization systems frequently hallucinate content that is not supported by the source document, mainly due to noise in the training dataset.Existing methods opt to drop the noisy samples or tokens from the training set entirely, reducing the effective training set size and creating an artificial propensity to copy words from the source.In this work, we propose a training objective for abstractive summarization based on rejection learning, in which the model learns whether or not to reject potentially noisy tokens.We further propose a regularized decoding objective that penalizes non-factual candidate summaries during inference by using the rejection probability learned during training.We show that our method considerably improves the factuality of generated summaries in automatic and human evaluations when compared to five baseline models, and that it does so while increasing the abstractiveness of the generated summaries.1 Source: (...) Chris Cox, the university's director of development, said the research centre would translate discoveries made in the laboratory into new treatments.He said it would house 150 additional researchers who will be developing new ideas and treatments.Research will focus on radiation therapy, lung cancer, women's cancers, melanoma and haematological oncology.The centre is the result of a partnership between The University of Manchester, The Christie NHS Foundation Trust and Cancer Research UK.The Christie's chief executive Caroline Shaw said the funding would "help facilitate groundbreaking research right here in Manchester".(...)

Neural abstractive summarization systems have achieved promising progress, thanks to the availability of large-scale datasets and models pre-trained with self-supervised methods.However, ensuring the factual consistency of the generated summaries for abstractive summarization systems is a challenge.We propose a post-editing corrector module to address this issue by identifying and correcting factual errors in generated summaries.The neural corrector model is pre-trained on artificial examples that are created by applying a series of heuristic transformations on reference summaries.These transformations are inspired by an error analysis of state-of-the-art summarization model outputs.Experimental results show that our model is able to correct factual errors in summaries generated by other neural summarization models and outperforms previous models on factual consistency evaluation on the CNN/DailyMail dataset.We also find that transferring from artificial error correction to downstream settings is still very challenging 1 .Article: Jerusalem (CNN)The flame of remembrance burns in Jerusalem, and a song of memory haunts Valerie Braham as it never has before.(...) "Now I truly understand everyone who has lost a loved one," Braham said.Her husband, Philippe Braham, was one of 17 people killed in January's terror attacks in Paris.He was in a kosher supermarket when a gunman stormed in, killing four people, all of them Jewish.(...) Original: Valerie braham was one of 17 people killed in january's terror attacks in paris.(inconsistent) Corrected: Philippe braham was one of 17 people killed in january's terror attacks in paris.(consistent) Article: (...) Thursday's attack by al-Shabaab militants killed 147 people, including 142 students, three security officers and two university security personnel.The attack left 104 people injured, including 19 who are in critical condition, Nkaissery said.(...) Original: 147 people, including 142 students, are in critical condition.(inconsistent) Corrected: 19 people, including 142 students, are in critical condition.(inconsistent) Article: (CNN) Officer Michael Slager's five-year career with the North Charleston Police Department in South Carolina ended after he resorted to deadly force following a routine traffic stop.(...) His back is to Slager, who, from a few yards away, raises his gun and fires.Slager is now charged with murder.The FBI is involved in the investigation of the slaying of the father of four.(...) Original: Slager is now charged with murder.(consistent) Corrected: Michael Slager is now charged with murder.(consistent) Article: (CNN)The announcement this year of a new, original Dr. Seuss book sent a wave of nostalgic giddiness across Twitter, and months before publication, the number of pre-orders for "What Pet Should I Get?" continues to climb.(...) It features the spirited siblings from the beloved classic "One Fish Two Fish Red Fish Blue Fish" and is believed to have been written between 1958 and 1962.(...) Original: Seuss book sent a wave of nostalgic giddiness across twitter.(consistent) Corrected: "One Fish Two Fish Red Fish Blue Fish" book sent a wave of nostalgic giddiness across twitter.(inconsistent)

Inferring missing facts in temporal knowledge graphs (TKGs) is a fundamental and challenging task.Previous works have approached this problem by augmenting methods for static knowledge graphs to leverage time-dependent representations.However, these methods do not explicitly leverage multi-hop structural information and temporal facts from recent time steps to enhance their predictions.Additionally, prior work does not explicitly address the temporal sparsity and variability of entity distributions in TKGs.We propose the Temporal Message Passing (TeMP) framework to address these challenges by combining graph neural networks, temporal dynamics models, data imputation and frequency-based gating techniques.Experiments 1 on standard TKG tasks show that our approach provides substantial gains compared to the previous state of the art, achieving a 10.7% average relative improvement in Hits@10 across three standard benchmarks.Our analysis also reveals important sources of variability both within and across TKG datasets, and we introduce several simple but strong baselines that outperform the prior state of the art in certain settings.

Meng Cao, Jackie Chi Kit Cheung. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.