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Found 24 papers, 11 papers with code
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
A promising approach is reinforcement learning from human feedback (RLHF), which leverages human feedback to update the model in accordance with human preferences and mitigate issues like toxicity and hallucinations.
GEO: Generative Engine Optimization
We facilitate systematic evaluation in this new paradigm by introducing GEO-bench, a benchmark of diverse user queries across multiple domains, coupled with sources required to answer these queries.
Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs
Our experiments with ChatGPT-3. 5 show that this bias is ubiquitous - 80% of our personas demonstrate bias; it is significant - some datasets show performance drops of 70%+; and can be especially harmful for certain groups - some personas suffer statistically significant drops on 80%+ of the datasets.
QualEval: Qualitative Evaluation for Model Improvement
In this work, we address the shortcomings of quantitative metrics by proposing QualEval, which augments quantitative scalar metrics with automated qualitative evaluation as a vehicle for model improvement.
Distraction-free Embeddings for Robust VQA
The generation of effective latent representations and their subsequent refinement to incorporate precise information is an essential prerequisite for Vision-Language Understanding (VLU) tasks such as Video Question Answering (VQA).
InstructEval: Systematic Evaluation of Instruction Selection Methods
In-context learning (ICL) performs tasks by prompting a large language model (LLM) using an instruction and a small set of annotated examples called demonstrations.
C-STS: Conditional Semantic Textual Similarity
Semantic textual similarity (STS), a cornerstone task in NLP, measures the degree of similarity between a pair of sentences, and has broad application in fields such as information retrieval and natural language understanding.
Anthropomorphization of AI: Opportunities and Risks
With widespread adoption of AI systems, and the push from stakeholders to make it human-like through alignment techniques, human voice, and pictorial avatars, the tendency for users to anthropomorphize it increases significantly.
Toxicity in ChatGPT: Analyzing Persona-assigned Language Models
Large language models (LLMs) have shown incredible capabilities and transcended the natural language processing (NLP) community, with adoption throughout many services like healthcare, therapy, education, and customer service.
MUX-PLMs: Data Multiplexing for High-throughput Language Models
The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies.
SemSup-XC: Semantic Supervision for Zero and Few-shot Extreme Classification
In this paper, we develop SemSup-XC, a model that achieves state-of-the-art zero-shot and few-shot performance on three XC datasets derived from legal, e-commerce, and Wikipedia data.
SPARTAN: Sparse Hierarchical Memory for Parameter-Efficient Transformers
Fine-tuning pre-trained language models (PLMs) achieves impressive performance on a range of downstream tasks, and their sizes have consequently been getting bigger.
ALIGN-MLM: Word Embedding Alignment is Crucial for Multilingual Pre-training
In particular, ALIGN-MLM outperforms XLM and MLM by 35 and 30 F1 points on POS-tagging for transfer between languages that differ both in their script and word order (left-to-right v. s.
SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization
Prior work along this vein have largely used expensive per-instance annotation or singular class-level descriptions, but per-instance descriptions are hard to scale and single class descriptions may not be rich enough.
When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer
While recent work on multilingual language models has demonstrated their capacity for cross-lingual zero-shot transfer on downstream tasks, there is a lack of consensus in the community as to what shared properties between languages enable such transfer.
Guiding Attention for Self-Supervised Learning with Transformers
In this paper, we propose a simple and effective technique to allow for efficient self-supervised learning with bi-directional Transformers.
Sentiment Analysis for Reinforcement Learning
Furthermore, this can enable reinforcement learning without rewards, in which the agent learns entirely from these intrinsic sentiment rewards.
Evaluating a Generative Adversarial Framework for Information Retrieval
Recent advances in Generative Adversarial Networks (GANs) have resulted in its widespread applications to multiple domains.
CLEVR Parser: A Graph Parser Library for Geometric Learning on Language Grounded Image Scenes
The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains.
Dissecting an Adversarial framework for Information Retrieval
Recent advances in Generative Adversarial Networks facilitated by improvements to the framework and successful application to various problems has resulted in extensions to multiple domains.
Discovering hierarchies using Imitation Learning from hierarchy aware policies
Learning options that allow agents to exhibit temporally higher order behavior has proven to be useful in increasing exploration, reducing sample complexity and for various transfer scenarios.
Improvements on Hindsight Learning
One such approach is Hindsight Experience replay which uses an off-policy Reinforcement Learning algorithm to learn a goal conditioned policy.
FigureNet: A Deep Learning model for Question-Answering on Scientific Plots
Deep Learning has managed to push boundaries in a wide variety of tasks.
Weight Initialization in Neural Language Models
Ontological methods are good at encoding Semantic Similarity and Vector Space models are better at encoding Semantic Relatedness.
