First, by using LFMs to identify desirable behaviour to imitate, we improve in task-completion rate over strong behavioural cloning baselines on three distinct language grounding environments (Touchdown, ScienceWorld, and ALFWorld). Second, imitation learning using LFMs outperform using LLMs as experts to directly predict actions, when controlling for the number of LLM output tokens.

We introduce Language Feedback Models (LFMs) that identify desirable behaviour --- actions that help achieve tasks specified in the instruction - for imitation learning in instruction following. To train LFMs, we obtain feedback from Large Language Models (LLMs) on visual trajectories verbalized to language descriptions. First, by using LFMs to identify desirable behaviour to imitate, we improve in task-completion rate over strong behavioural cloning baselines on three distinct language grounding environments (Touchdown, ScienceWorld, and ALFWorld). Second, LFMs outperform using LLMs as experts to directly predict actions, when controlling for the number of LLM output tokens. Third, LFMs generalize to unseen environments, improving task-completion rate by 3.5-12.0%

While some biological neural networks are well known, we expect that the vast majority remain undiscovered due to the enormous variety of tasks the brain performs. Many methods have been developed to help discover latent networks of neural populations (i.e.

Large language models (LLMs) have demonstrated impressive capabilities for many coding tasks, including summarization, translation, completion, and code generation. However, detecting code vulnerabilities remains a challenging task for LLMs. An effective way to improve LLM performance is in-context learning (ICL) - providing few-shot examples similar to the query, along with correct answers, can improve an LLM's ability to generate correct solutions. However, choosing the few-shot examples appropriately is crucial to improving model performance. In this paper, we explore two criteria for choosing few-shot examples for ICL used in the code vulnerability detection task. The first criterion considers if the LLM (consistently) makes a mistake or not on a sample with the intuition that LLM performance on a sample is informative about its usefulness as a few-shot example. The other criterion considers similarity of the examples with the program under query and chooses few-shot examples based on the $k$-nearest neighbors to the given sample. We perform evaluations to determine the benefits of these criteria individually as well as under various combinations, using open-source models on multiple datasets.

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper proposes a novel model selection criterion for binary latent feature models. It is like variational Bayes, except that rather than assuming a factorized posterior over latent variables and parameters, it approximately integrates out the parameters using the BIC. They demonstrate improved held-out likelihood scores compared to several existing IBP implementations. The proposed approach seems like a reasonable thing to do, and is motivated by a plausible asymptotic argument.

This paper extends factorized asymptotic Bayesian (FAB) inference for latent feature models~(LFMs). FAB inference has not been applicable to models, including LFMs, without a specific condition on the Hesqsian matrix of a complete log-likelihood, which is required to derive a factorized information criterion''~(FIC). Our asymptotic analysis of the Hessian matrix of LFMs shows that FIC of LFMs has the same form as those of mixture models. FAB/LFMs have several desirable properties (e.g., automatic hidden states selection and parameter identifiability) and empirically perform better than state-of-the-art Indian Buffet processes in terms of model selection, prediction, and computational efficiency.