Carlos Jimenez


2023

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MUX-PLMs: Data Multiplexing for High-throughput Language Models
Vishvak Murahari | Ameet Deshpande | Carlos Jimenez | Izhak Shafran | Mingqiu Wang | Yuan Cao | Karthik Narasimhan
Findings of the Association for Computational Linguistics: EMNLP 2023

The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasing model sizes coupled with hardware shortages has limited affordable access and poses a pressing need for efficiency approaches geared towards high throughput and performance. Multi-input multi-output (MIMO) algorithms such as data multiplexing, offer a promising solution with a many-fold increase in throughput by performing inference for multiple inputs at the cost of a single input. Yet these approaches are not currently performant enough to be deployed in modern systems. We change that by developing MUX-PLMs, a class of high throughput pre-trained language models (PLMs) trained with data multiplexing, that can be fine-tuned for any downstream task to yield high-throughput high-performance. Our novel multiplexing and demultiplexing modules proficiently entangle and disentangle inputs, and enable high-performance high throughput MUX-PLMs that are competitive with vanilla PLMs while achieving 2x/5x inference speedup with only a 1-4 % drop on a broad suite of tasks.

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C-STS: Conditional Semantic Textual Similarity
Ameet Deshpande | Carlos Jimenez | Howard Chen | Vishvak Murahari | Victoria Graf | Tanmay Rajpurohit | Ashwin Kalyan | Danqi Chen | Karthik Narasimhan
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Semantic textual similarity (STS) has been a cornerstone task in NLP that measures the degree of similarity between a pair of sentences, with applications in information retrieval, question answering, and embedding methods. However, it is an inherently ambiguous task, with the sentence similarity depending on the specific aspect of interest. We resolve this ambiguity by proposing a novel task called conditional STS (C-STS) which measures similarity conditioned on an aspect elucidated in natural language (hereon, condition). As an example, the similarity between the sentences “The NBA player shoots a three-pointer.” and “A man throws a tennis ball into the air to serve.” is higher for the condition “The motion of the ball.” (both upward) and lower for “The size of the ball.” (one large and one small). C-STS’s advantages are two-fold: (1) it reduces the subjectivity and ambiguity of STS, and (2) enables fine-grained similarity evaluation using diverse conditions. C-STS contains almost 20,000 instances from diverse domains and we evaluate several state-of-the-art models to demonstrate that even the most performant fine-tuning and in-context learning models (GPT-4, Flan, SimCSE) find it challenging, with Spearman correlation scores of <50. We encourage the community to evaluate their models on C-STS to provide a more holistic view of semantic similarity and natural language understanding.

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MUX-PLMs: Pre-training Language Models with Data Multiplexing
Vishvak Murahari | Ameet Deshpande | Carlos Jimenez | Izhak Shafran | Mingqiu Wang | Yuan Cao | Karthik Narasimhan
Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)

The widespread adoption of large language models such as ChatGPT and Bard has led to unprecedented demand for these technologies. The burgeoning cost of inference for ever-increasing model sizes coupled with hardware shortages has limited affordable access and poses a pressing need for efficiency approaches geared towards high throughput and performance. Multi-input multi-output (MIMO) algorithms such as data multiplexing, offer a promising solution with a many-fold increase in throughput by performing inference for multiple inputs at the cost of a single input. Yet these approaches are not currently performant enough to be deployed in modern systems. We change that by developing MUX-PLMs, a class of high throughput pre-trained language models (PLMs) trained with data multiplexing, that can be fine-tuned for any downstream task to yield high-throughput high-performance. Our novel multiplexing and demultiplexing modules proficiently entangle and disentangle inputs, and enable high-performance high throughput that are competitive with vanilla PLMs while achieving 2x/5x inference speedup with only a 1−4% drop on a broad suite of tasks.