163 citations · 240 across the 24 of their papers we have counts for
24 papers
The Bias is in the Details: An Assessment of Cognitive Bias in LLMs
R. Alexander Knipper, Charles S. Knipper, Kaiqi Zhang +3
As Large Language Models (LLMs) are increasingly embedded in real-world decision-making processes, it becomes crucial to examine the extent to which they exhibit cognitive biases.…
EfficientXLang: Towards Improving Token Efficiency Through Cross-Lingual Reasoning
Sanchit Ahuja, Praneetha Vaddamanu, Barun Patra
Despite recent advances in Language Reasoning Models (LRMs), most research focuses solely on English, even though many models are pretrained on multilingual data. In this work, we…
Hummingbird: High Fidelity Image Generation via Multimodal Context Alignment
Minh-Quan Le, Gaurav Mittal, Tianjian Meng +5
While diffusion models are powerful in generating high-quality, diverse synthetic data for object-centric tasks, existing methods struggle with scene-aware tasks such as Visual Que…
On The Adaptation of Unlimiformer for Decoder-Only Transformers
Kian Ahrabian, Alon Benhaim, Barun Patra +3
One of the prominent issues stifling the current generation of large language models is their limited context length. Recent proprietary models such as GPT-4 and Claude 2 have intr…
Scaling Laws for Multilingual Language Models
Yifei He, Alon Benhaim, Barun Patra +6
We propose a novel scaling law for general-purpose decoder-only language models (LMs) trained on multilingual data, tackling the problem of balancing languages during multilingual…
S2-Attention: Hardware-Aware Context Sharding Among Attention Heads
Xihui Lin, Yunan Zhang, Suyu Ge +5
Sparse attention, which selectively attends to a subset of tokens in the context was supposed to be efficient. However, its theoretical reduction in FLOPs has rarely translated int…
A Practical Analysis of Human Alignment with *PO
Kian Ahrabian, Xihui Lin, Barun Patra +4
At the forefront of state-of-the-art human alignment methods are preference optimization methods (*PO). Prior research has often concentrated on identifying the best-performing met…
sPhinX: Sample Efficient Multilingual Instruction Fine-Tuning Through N-shot Guided Prompting
Sanchit Ahuja, Kumar Tanmay, Hardik Hansrajbhai Chauhan +9
Despite the remarkable success of large language models (LLMs) in English, a significant performance gap remains in non-English languages. To address this, we introduce a novel app…
Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone
Marah Abdin, Jyoti Aneja, Hany Awadalla +126
We introduce phi-3-mini, a 3.8 billion parameter language model trained on 3.3 trillion tokens, whose overall performance, as measured by both academic benchmarks and internal test…
A Glitch in the Matrix? Locating and Detecting Language Model Grounding with Fakepedia
Giovanni Monea, Maxime Peyrard, Martin Josifoski +6
Large language models (LLMs) have an impressive ability to draw on novel information supplied in their context. Yet the mechanisms underlying this contextual grounding remain unkno…
A Length-Extrapolatable Transformer
Yutao Sun, Li Dong, Barun Patra +6
Position modeling plays a critical role in Transformers. In this paper, we focus on length extrapolation, i.e., training on short texts while evaluating longer sequences. We define…
TorchScale: Transformers at Scale
Shuming Ma, Hongyu Wang, Shaohan Huang +8
Large Transformers have achieved state-of-the-art performance across many tasks. Most open-source libraries on scaling Transformers focus on improving training or inference with be…
Beyond English-Centric Bitexts for Better Multilingual Language Representation Learning
Barun Patra, Saksham Singhal, Shaohan Huang +5
In this paper, we elaborate upon recipes for building multilingual representation models that are not only competitive with existing state-of-the-art models but are also more param…
Foundation Transformers
Hongyu Wang, Shuming Ma, Shaohan Huang +12
A big convergence of model architectures across language, vision, speech, and multimodal is emerging. However, under the same name "Transformers", the above areas use different imp…
Language Model Decoding as Likelihood-Utility Alignment
Martin Josifoski, Maxime Peyrard, Frano Rajic +8
A critical component of a successful language generation pipeline is the decoding algorithm. However, the general principles that should guide the choice of a decoding algorithm re…
On Efficiently Acquiring Annotations for Multilingual Models
Joel Ruben Antony Moniz, Barun Patra, Matthew R. Gormley
When tasked with supporting multiple languages for a given problem, two approaches have arisen: training a model for each language with the annotation budget divided equally among…
On the Representation Collapse of Sparse Mixture of Experts
Zewen Chi, Li Dong, Shaohan Huang +9
Sparse mixture of experts provides larger model capacity while requiring a constant computational overhead. It employs the routing mechanism to distribute input tokens to the best-…
Invariant Language Modeling
Maxime Peyrard, Sarvjeet Singh Ghotra, Martin Josifoski +5
Large pretrained language models are critical components of modern NLP pipelines. Yet, they suffer from spurious correlations, poor out-of-domain generalization, and biases. Inspir…
To Schedule or not to Schedule: Extracting Task Specific Temporal Entities and Associated Negation Constraints
Barun Patra, Chala Fufa, Pamela Bhattacharya +1
State of the art research for date-time entity extraction from text is task agnostic. Consequently, while the methods proposed in literature perform well for generic date-time extr…
ScopeIt: Scoping Task Relevant Sentences in Documents
Vishwas Suryanarayanan, Barun Patra, Pamela Bhattacharya +2
Intelligent assistants like Cortana, Siri, Alexa, and Google Assistant are trained to parse information when the conversation is synchronous and short; however, for email-based con…
Bilingual Lexicon Induction with Semi-supervision in Non-Isometric Embedding Spaces
Barun Patra, Joel Ruben Antony Moniz, Sarthak Garg +2
Recent work on bilingual lexicon induction (BLI) has frequently depended either on aligned bilingual lexicons or on distribution matching, often with an assumption about the isomet…
Compression and Localization in Reinforcement Learning for ATARI Games
Joel Ruben Antony Moniz, Barun Patra, Sarthak Garg
Deep neural networks have become commonplace in the domain of reinforcement learning, but are often expensive in terms of the number of parameters needed. While compressing deep ne…
Towards Understanding and Answering Multi-Sentence Recommendation Questions on Tourism
Danish Contractor, Barun Patra, Mausam Singla +1
We introduce the first system towards the novel task of answering complex multisentence recommendation questions in the tourism domain. Our solution uses a pipeline of two modules:…
A survey of Community Question Answering
Barun Patra
With the advent of numerous community forums, tasks associated with the same have gained importance in the recent past. With the influx of new questions every day on these forums,…