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Found 14 papers, 6 papers with code
PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics
Recently, long chain of thought (LCoT), Large Language Models (LLMs), have taken the machine learning world by storm with their breathtaking reasoning capabilities.
An Empirical Study on Strong-Weak Model Collaboration for Repo-level Code Generation
We study cost-efficient collaboration between strong and weak language models for repository-level code generation, where the weak model handles simpler tasks at lower cost, and the most challenging tasks are delegated to the strong model.
Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction
Historical linguists have long written "programs" that convert reconstructed words in an ancestor language into their attested descendants via ordered string rewrite functions (called sound laws) However, writing these programs is time-consuming, motivating the development of automated Sound Law Induction (SLI) which we formulate as Programming by Examples (PBE) with Large Language Models (LLMs) in this paper.
CRScore: Grounding Automated Evaluation of Code Review Comments in Code Claims and Smells
The task of automated code review has recently gained a lot of attention from the machine learning community.
Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction
Historical linguists have long written a kind of incompletely formalized ''program'' that converts reconstructed words in an ancestor language into words in one of its attested descendants that consist of a series of ordered string rewrite functions (called sound laws).
Generating Situated Reflection Triggers about Alternative Solution Paths: A Case Study of Generative AI for Computer-Supported Collaborative Learning
An advantage of Large Language Models (LLMs) is their contextualization capability - providing different responses based on student inputs like solution strategy or prior discussion, to potentially better engage students than standard feedback.
On the Limitations of Embedding Based Methods for Measuring Functional Correctness for Code Generation
The task of code generation from natural language (NL2Code) has become extremely popular, especially with the advent of Large Language Models (LLMs).
Data Augmentation for Code Translation with Comparable Corpora and Multiple References
One major challenge of translating code between programming languages is that parallel training data is often limited.
Tricking LLMs into Disobedience: Formalizing, Analyzing, and Detecting Jailbreaks
Recent explorations with commercial Large Language Models (LLMs) have shown that non-expert users can jailbreak LLMs by simply manipulating their prompts; resulting in degenerate output behavior, privacy and security breaches, offensive outputs, and violations of content regulator policies.
Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks
This large and diverse collection of tasks enables rigorous benchmarking of cross-task generalization under instructions -- training models to follow instructions on a subset of tasks and evaluating them on the remaining unseen ones.
Representation Learning for Conversational Data using Discourse Mutual Information Maximization
Although many pretrained models exist for text or images, there have been relatively fewer attempts to train representations specifically for dialog understanding.
Weakly Supervised Explainable Phrasal Reasoning with Neural Fuzzy Logic
In this work, we address the explainability of NLI by weakly supervised logical reasoning, and propose an Explainable Phrasal Reasoning (EPR) approach.
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach
For the regression task, VADEC, when trained with SenWave, achieves 7. 6% and 16. 5% gains in Pearson Correlation scores over the current state-of-the-art on the EMOBANK dataset for the Valence (V) and Dominance (D) affect dimensions respectively.
How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian Emotions Through The Lens of Twitter
Since its outbreak, the ongoing COVID-19 pandemic has caused unprecedented losses to human lives and economies around the world.
