319 research outputs found
ECHo: A Visio-Linguistic Dataset for Event Causality Inference via Human-Centric Reasoning
We introduce ECHo (Event Causality Inference via Human-Centric Reasoning), a
diagnostic dataset of event causality inference grounded in visio-linguistic
social scenarios. ECHo employs real-world human-centric deductive information
building on a television crime drama. ECHo requires the Theory-of-Mind (ToM)
ability to understand and reason about social interactions based on multimodal
information. Using ECHo, we propose a unified Chain-of-Thought (CoT) framework
to assess the reasoning capability of current AI systems. Our ToM-enhanced CoT
pipeline accommodates various large foundation models in both zero-shot and
few-shot visio-linguistic reasoning. We use this framework to scrutinize recent
large foundation models such as InstructGPT and MiniGPT-4 on three diagnostic
human-centric tasks. Further analysis demonstrates ECHo as a challenging
dataset to expose imperfections and inconsistencies in reasoning. Our data and
code are publicly available at https://github.com/YuxiXie/ECHo.Comment: Findings of EMNLP 2023. 10 pages, 6 figures, 5 tables (22 pages, 8
figures, 15 tables including references and appendices
Automatic Model Selection with Large Language Models for Reasoning
Chain-of-Thought (CoT) and Program-Aided Language Models (PAL) represent two
distinct reasoning methods, each with its own strengths. CoT employs natural
language, offering flexibility and interpretability, while PAL utilizes
programming language, yielding more structured and rigorous logic. We introduce
a model selection method to combine the best of both worlds by employing a
large language model (LLM) to dynamically select between them. Our theoretical
analysis underscores the feasibility of this method, which is further
corroborated by empirical results. Our proposed method demonstrates significant
performance improvements across eight reasoning datasets with Codex, ChatGPT,
and GPT-4. Additionally, our method is complementary to self-consistency; when
integrated, it can further enhance performance while significantly reducing
computation costs. Moreover, we achieve new state-of-the-art results on GSM8K
and SVAMP, with respective accuracies of 96.8% and 93.7%. Our code, data and
prompts are available at https://github.com/XuZhao0/Model-Selection-ReasoningComment: EMNLP 2023 Finding
Photocatalytic Removal of Organics over BiVO4-Based Photocatalysts
Organic compounds, such as organic dyes and phenols, are the main pollutants in wastewater. In the past years, a large number of studies on the fabrication and photocatalytic organics degradation of BiVO4 and its related materials have been reported in the literature. In this chapter, we shall focus on the advancements in the synthesis and photocatalytic applications of several kinds of BiVO4-based photocatalysts: (i) well-defined morphological BiVO4 photocatalysts, (ii) porous BiVO4 photocatalysts, (iii) heteroatom-doped BiVO4 photocatalysts, (iv) BiVO4-based heterojunction photocatalysts, and (v) supported BiVO4 photocatalysts. We shall discuss the structure–photocatalytic performance relationship of the materials and the involved photocatalytic degradation mechanisms. In addition, we also propose the research trends and technologies for practical applications of the BiVO4-based photocatalytic materials
Decomposition Enhances Reasoning via Self-Evaluation Guided Decoding
We endow Large Language Models (LLMs) with fine-grained self-evaluation to
refine multi-step reasoning inference. We propose an effective prompting
approach that integrates self-evaluation guidance through stochastic beam
search. Our approach explores the reasoning search space using a
well-calibrated automatic criterion. This enables an efficient search to
produce higher-quality final predictions. With the self-evaluation guided
stochastic beam search, we also balance the quality-diversity trade-off in the
generation of reasoning chains. This allows our approach to adapt well with
majority voting and surpass the corresponding Codex-backboned baselines by
, , and on the GSM8K, AQuA, and StrategyQA benchmarks,
respectively, in few-shot accuracy. Analysis of our decompositional reasoning
finds it pinpoints logic failures and leads to higher consistency and
robustness. Our code is publicly available at
https://github.com/YuxiXie/SelfEval-Guided-Decoding.Comment: Our code is publicly available at
https://github.com/YuxiXie/SelfEval-Guided-Decodin
InstructCoder: Empowering Language Models for Code Editing
Code editing encompasses a variety of pragmatic tasks that developers deal
with daily. Despite its relevance and practical usefulness, automatic code
editing remains an underexplored area in the evolution of deep learning models,
partly due to data scarcity. In this work, we explore the use of large language
models (LLMs) to edit code based on user instructions, covering a broad range
of implicit tasks such as comment insertion, code optimization, and code
refactoring. To facilitate this, we introduce InstructCoder, the first dataset
designed to adapt LLMs for general-purpose code editing, containing
highdiversity code-editing tasks. It consists of over 114,000
instruction-input-output triplets and covers multiple distinct code editing
scenarios. The dataset is systematically expanded through an iterative process
that commences with code editing data sourced from GitHub commits as seed
tasks. Seed and generated tasks are used subsequently to prompt ChatGPT for
more task data. Our experiments demonstrate that open-source LLMs fine-tuned on
InstructCoder can edit code correctly based on users' instructions most of the
time, exhibiting unprecedented code-editing performance levels. Such results
suggest that proficient instruction-finetuning can lead to significant
amelioration in code editing abilities. The dataset and the source code are
available at https://github.com/qishenghu/CodeInstruct
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