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Found 19 papers, 7 papers with code
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive Learning for Code Generation
With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are increasing interests in distilling the capabilies of close-sourced LLMs to smaller open-sourced LLMs.
CodeChain: Towards Modular Code Generation Through Chain of Self-revisions with Representative Sub-modules
We find that by naturally encouraging the LLM to reuse the previously developed and verified sub-modules, CodeChain can significantly boost both modularity as well as correctness of the generated solutions, achieving relative pass@1 improvements of 35% on APPS and 76% on CodeContests.
AI for IT Operations (AIOps) on Cloud Platforms: Reviews, Opportunities and Challenges
There are a wide variety of problems to address, and multiple use-cases, where AI capabilities can be leveraged to enhance operational efficiency.
LogAI: A Library for Log Analytics and Intelligence
In order to enable users to perform multiple types of AI-based log analysis tasks in a uniform manner, we introduce LogAI (https://github. com/salesforce/logai), a one-stop open source library for log analytics and intelligence.
Learning Label Modular Prompts for Text Classification in the Wild
Machine learning models usually assume i. i. d data during training and testing, but data and tasks in real world often change over time.
Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding
VIP particularly focuses on two aspects -- contextual prompts that learns input-specific contextualization of the soft prompt tokens through a small-scale sentence encoder and quantized prompts that maps the contextualized prompts to a set of learnable codebook vectors through a Vector quantization network.
Mining Root Cause Knowledge from Cloud Service Incident Investigations for AIOps
ICA forms the backbone of a simple-yet-effective Retrieval based RCA for new incidents, through an Information Retrieval system to search and rank past incidents and detect likely root causes from them, given the incident symptom.
Merlion: A Machine Learning Library for Time Series
We introduce Merlion, an open-source machine learning library for time series.
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic form of numerical reasoning over text in Machine Reading Comprehension (MRC).
Scene Graph based Image Retrieval -- A case study on the CLEVR Dataset
With the prolification of multimodal interaction in various domains, recently there has been much interest in text based image retrieval in the computer vision community.
Complex Program Induction for Querying Knowledge Bases in the Absence of Gold Programs
On one of the hardest class of programs (comparative reasoning) with 5{--}10 steps, CIPITR outperforms NSM by a factor of 89 and memory networks by 9 times.
DuoRC: Towards Complex Language Understanding with Paraphrased Reading Comprehension
We propose DuoRC, a novel dataset for Reading Comprehension (RC) that motivates several new challenges for neural approaches in language understanding beyond those offered by existing RC datasets.
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG.
Stance Classification of Context-Dependent Claims
Recent work has addressed the problem of detecting relevant claims for a given controversial topic.
Towards Building Large Scale Multimodal Domain-Aware Conversation Systems
With this dataset, we propose 5 new sub-tasks for multimodal conversations along with their evaluation methodology.
A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation
However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications).
Taxonomy grounded aggregation of classifiers with different label sets
We describe the problem of aggregating the label predictions of diverse classifiers using a class taxonomy.
An Autoencoder Approach to Learning Bilingual Word Representations
Cross-language learning allows us to use training data from one language to build models for a different language.
