Orionnnn/hub-stats huggingface dataset:
Leveraging Large Language Models in Conversational Recommender Systems
Authors: name: Luke Friedman, name: Sameer Ahuja, name: David Allen, name: Terry Tan, name: Hakim Sidahmed, name: Changbo Long, name: Jun Xie, name: Gabriel Schubiner, name: Ajay Patel, name: Harsh Lara, name: Brian Chu, name: Zexi Chen, name: Manoj Tiwari
Published: 2023-05-13 16:40:07

A Conversational Recommender System (CRS) offers increased transparency and
control to users by enabling them to engage with the system through a real-time
multi-turn dialogue. Recently, Large Language Models (LLMs) have exhibited an
unprecedented ability to converse naturally and incorporate world knowledge and
common-sense reasoning into language understanding, unlocking the potential of
this paradigm. However, effectively leveraging LLMs within a CRS introduces new
technical challenges, including properly understanding and controlling a
complex conversation and retrieving from external sources of information. These
issues are exacerbated by a large, evolving item corpus and a lack of
conversational data for training. In this paper, we provide a roadmap for
building an end-to-end large-scale CRS using LLMs. In particular, we propose
new implementations for user preference understanding, flexible dialogue
management and explainable recommendations as part of an integrated
architecture powered by LLMs. For improved personalization, we describe how an
LLM can consume interpretable natural language user profiles and use them to
modulate session-level context. To overcome conversational data limitations in
the absence of an existing production CRS, we propose techniques for building a
controllable LLM-based user simulator to generate synthetic conversations. As a
proof of concept we introduce RecLLM, a large-scale CRS for YouTube videos
built on LaMDA, and demonstrate its fluency and diverse functionality through
some illustrative example conversations.
CodeT5+: Open Code Large Language Models for Code Understanding and
  Generation
Authors: name: Yue Wang, name: Hung Le, name: Akhilesh Deepak Gotmare, name: Nghi D. Q. Bui, name: Junnan Li, name: Steven C. H. Hoi
Published: 2023-05-13 14:23:07

Large language models (LLMs) pretrained on vast source code have achieved
prominent progress in code intelligence. However, existing code LLMs have two
main limitations in terms of architecture and pretraining tasks. First, they
often adopt a specific architecture (encoder-only or decoder-only) or rely on a
unified encoder-decoder network for different downstream tasks. The former
paradigm is limited by inflexibility in applications while in the latter, the
model is treated as a single system for all tasks, leading to suboptimal
performance on a subset of tasks. Secondly, they often employ a limited set of
pretraining objectives which might not be relevant to some downstream tasks and
hence result in substantial performance degrade. To address these limitations,
we propose ``CodeT5+'', a family of encoder-decoder LLMs for code in which
component modules can be flexibly combined to suit a wide range of downstream
code tasks. Such flexibility is enabled by our proposed mixture of pretraining
objectives to mitigate the pretrain-finetune discrepancy. These objectives
cover span denoising, contrastive learning, text-code matching, and causal LM
pretraining tasks, on both unimodal and bimodal multilingual code corpora.
Furthermore, we propose to initialize CodeT5+ with frozen off-the-shelf LLMs
without training from scratch to efficiently scale up our models, and explore
instruction-tuning to align with natural language instructions. We extensively
evaluate CodeT5+ on over 20 code-related benchmarks in different settings,
including zero-shot, finetuning, and instruction-tuning. We observe
state-of-the-art (SoTA) model performance on various code-related tasks, such
as code generation and completion, math programming, and text-to-code retrieval
tasks. Particularly, our instruction-tuned CodeT5+ 16B achieves new SoTA
results on HumanEval code generation task against other open code LLMs.
Dr. LLaMA: Improving Small Language Models in Domain-Specific QA via
  Generative Data Augmentation
Authors: name: Zhen Guo, name: Peiqi Wang, name: Yanwei Wang, name: Shangdi Yu
Published: 2023-05-12 23:49:23

Large Language Models (LLMs) have made significant strides in natural
language processing but face challenges in terms of computational expense and
inefficiency as they grow in size, especially in domain-specific tasks. Small
Language Models (SLMs), on the other hand, often struggle in these tasks due to
limited capacity and training data. In this paper, we introduce Dr. LLaMA, a
method for improving SLMs through generative data augmentation using LLMs,
focusing on medical question-answering tasks and the PubMedQA dataset. Our
findings indicate that LLMs effectively refine and diversify existing
question-answer pairs, resulting in improved performance of a much smaller
model on domain-specific QA datasets after fine-tuning. This study highlights
the challenges of using LLMs for domain-specific question answering and
suggests potential research directions to address these limitations, ultimately
aiming to create more efficient and capable models for specialized
applications. We have also made our code available for interested researchers
Masked Audio Text Encoders are Effective Multi-Modal Rescorers
Authors: name: Jinglun Cai, name: Monica Sunkara, name: Xilai Li, name: Anshu Bhatia, name: Xiao Pan, name: Sravan Bodapati
Published: 2023-05-11 22:44:43

Masked Language Models (MLMs) have proven to be effective for second-pass
rescoring in Automatic Speech Recognition (ASR) systems. In this work, we
propose Masked Audio Text Encoder (MATE), a multi-modal masked language model
rescorer which incorporates acoustic representations into the input space of
MLM. We adopt contrastive learning for effectively aligning the modalities by
learning shared representations. We show that using a multi-modal rescorer is
beneficial for domain generalization of the ASR system when target domain data
is unavailable. MATE reduces word error rate (WER) by 4%-16% on in-domain, and
3%-7% on out-of-domain datasets, over the text-only baseline. Additionally,
with very limited amount of training data (0.8 hours), MATE achieves a WER
reduction of 8%-23% over the first-pass baseline.
RL4F: Generating Natural Language Feedback with Reinforcement Learning
  for Repairing Model Outputs
Authors: name: Afra Feyza Akyürek, name: Ekin Akyürek, name: Aman Madaan, name: Ashwin Kalyan, name: Peter Clark, name: Derry Wijaya, name: Niket Tandon
Published: 2023-05-15 17:57:16

Despite their unprecedented success, even the largest language models make
mistakes. Similar to how humans learn and improve using feedback, previous work
proposed providing language models with natural language feedback to guide them
in repairing their outputs. Because human-generated critiques are expensive to
obtain, researchers have devised learned critique generators in lieu of human
critics while assuming one can train downstream models to utilize generated
feedback. However, this approach does not apply to black-box or limited access
models such as ChatGPT, as they cannot be fine-tuned. Moreover, in the era of
large general-purpose language agents, fine-tuning is neither computationally
nor spatially efficient as it results in multiple copies of the network. In
this work, we introduce RL4F (Reinforcement Learning for Feedback), a
multi-agent collaborative framework where the critique generator is trained to
maximize end-task performance of GPT-3, a fixed model more than 200 times its
size. RL4F produces critiques that help GPT-3 revise its outputs. We study
three datasets for action planning, summarization and alphabetization and show
improvements (~5% on average) in multiple text similarity metrics over strong
baselines across all three tasks.
GPT-Sentinel: Distinguishing Human and ChatGPT Generated Content
Authors: name: Yutian Chen, name: Hao Kang, name: Vivian Zhai, name: Liangze Li, name: Rita Singh, name: Bhiksha Ramakrishnan
Published: 2023-05-13 17:12:11

This paper presents a novel approach for detecting ChatGPT-generated vs.
human-written text using language models. To this end, we first collected and
released a pre-processed dataset named OpenGPTText, which consists of rephrased
content generated using ChatGPT. We then designed, implemented, and trained two
different models for text classification, using Robustly Optimized BERT
Pretraining Approach (RoBERTa) and Text-to-Text Transfer Transformer (T5),
respectively. Our models achieved remarkable results, with an accuracy of over
97% on the test dataset, as evaluated through various metrics. Furthermore, we
conducted an interpretability study to showcase our model's ability to extract
and differentiate key features between human-written and ChatGPT-generated
text. Our findings provide important insights into the effective use of
language models to detect generated text.
Small Models are Valuable Plug-ins for Large Language Models
Authors: name: Canwen Xu, name: Yichong Xu, name: Shuohang Wang, name: Yang Liu, name: Chenguang Zhu, name: Julian McAuley
Published: 2023-05-15 17:59:01

Large language models (LLMs) such as GPT-3 and GPT-4 are powerful but their
weights are often publicly unavailable and their immense sizes make the models
difficult to be tuned with common hardware. As a result, effectively tuning
these models with large-scale supervised data can be challenging. As an
alternative, In-Context Learning (ICL) can only use a small number of
supervised examples due to context length limits. In this paper, we propose
Super In-Context Learning (SuperICL) which allows black-box LLMs to work with
locally fine-tuned smaller models, resulting in superior performance on
supervised tasks. Our experiments demonstrate that SuperICL can improve
performance beyond state-of-the-art fine-tuned models while addressing the
instability problem of in-context learning. Furthermore, SuperICL can enhance
the capabilities of smaller models, such as multilinguality and
interpretability.
TinyStories: How Small Can Language Models Be and Still Speak Coherent
  English?
Authors: name: Ronen Eldan, name: Yuanzhi Li
Published: 2023-05-12 20:56:48

Language models (LMs) are powerful tools for natural language processing, but
they often struggle to produce coherent and fluent text when they are small.
Models with around 125M parameters such as GPT-Neo (small) or GPT-2 (small) can
rarely generate coherent and consistent English text beyond a few words even
after extensive training. This raises the question of whether the emergence of
the ability to produce coherent English text only occurs at larger scales (with
hundreds of millions of parameters or more) and complex architectures (with
many layers of global attention).
  In this work, we introduce TinyStories, a synthetic dataset of short stories
that only contain words that a typical 3 to 4-year-olds usually understand,
generated by GPT-3.5 and GPT-4. We show that TinyStories can be used to train
and evaluate LMs that are much smaller than the state-of-the-art models (below
10 million total parameters), or have much simpler architectures (with only one
transformer block), yet still produce fluent and consistent stories with
several paragraphs that are diverse and have almost perfect grammar, and
demonstrate reasoning capabilities.
  We also introduce a new paradigm for the evaluation of language models: We
suggest a framework which uses GPT-4 to grade the content generated by these
models as if those were stories written by students and graded by a (human)
teacher. This new paradigm overcomes the flaws of standard benchmarks which
often requires the model's output to be very structures, and moreover provides
a multidimensional score for the model, providing scores for different
capabilities such as grammar, creativity and consistency.
  We hope that TinyStories can facilitate the development, analysis and
research of LMs, especially for low-resource or specialized domains, and shed
light on the emergence of language capabilities in LMs.
Make-A-Protagonist: Generic Video Editing with An Ensemble of Experts
Authors: name: Yuyang Zhao, name: Enze Xie, name: Lanqing Hong, name: Zhenguo Li, name: Gim Hee Lee
Published: 2023-05-15 17:59:03

The text-driven image and video diffusion models have achieved unprecedented
success in generating realistic and diverse content. Recently, the editing and
variation of existing images and videos in diffusion-based generative models
have garnered significant attention. However, previous works are limited to
editing content with text or providing coarse personalization using a single
visual clue, rendering them unsuitable for indescribable content that requires
fine-grained and detailed control. In this regard, we propose a generic video
editing framework called Make-A-Protagonist, which utilizes textual and visual
clues to edit videos with the goal of empowering individuals to become the
protagonists. Specifically, we leverage multiple experts to parse source video,
target visual and textual clues, and propose a visual-textual-based video
generation model that employs mask-guided denoising sampling to generate the
desired output. Extensive results demonstrate the versatile and remarkable
editing capabilities of Make-A-Protagonist.
TESS: Text-to-Text Self-Conditioned Simplex Diffusion
Authors: name: Rabeeh Karimi Mahabadi, name: Jaesung Tae, name: Hamish Ivison, name: James Henderson, name: Iz Beltagy, name: Matthew E. Peters, name: Arman Cohan
Published: 2023-05-15 06:33:45

Diffusion models have emerged as a powerful paradigm for generation,
obtaining strong performance in various domains with continuous-valued inputs.
Despite the promises of fully non-autoregressive text generation, applying
diffusion models to natural language remains challenging due to its discrete
nature. In this work, we propose Text-to-text Self-conditioned Simplex
Diffusion (TESS), a text diffusion model that is fully non-autoregressive,
employs a new form of self-conditioning, and applies the diffusion process on
the logit simplex space rather than the typical learned embedding space.
Through extensive experiments on natural language understanding and generation
tasks including summarization, text simplification, paraphrase generation, and
question generation, we demonstrate that TESS outperforms state-of-the-art
non-autoregressive models and is competitive with pretrained autoregressive
sequence-to-sequence models.
Optimizing Memory Mapping Using Deep Reinforcement Learning
Authors: name: Pengming Wang, name: Mikita Sazanovich, name: Berkin Ilbeyi, name: Phitchaya Mangpo Phothilimthana, name: Manish Purohit, name: Han Yang Tay, name: Ngân Vũ, name: Miaosen Wang, name: Cosmin Paduraru, name: Edouard Leurent, name: Anton Zhernov, name: Julian Schrittwieser, name: Thomas Hubert, name: Robert Tung, name: Paula Kurylowicz, name: Kieran Milan, name: Oriol Vinyals, name: Daniel J. Mankowitz
Published: 2023-05-11 11:55:16

Resource scheduling and allocation is a critical component of many high
impact systems ranging from congestion control to cloud computing. Finding more
optimal solutions to these problems often has significant impact on resource
and time savings, reducing device wear-and-tear, and even potentially improving
carbon emissions. In this paper, we focus on a specific instance of a
scheduling problem, namely the memory mapping problem that occurs during
compilation of machine learning programs: That is, mapping tensors to different
memory layers to optimize execution time.
  We introduce an approach for solving the memory mapping problem using
Reinforcement Learning. RL is a solution paradigm well-suited for sequential
decision making problems that are amenable to planning, and combinatorial
search spaces with high-dimensional data inputs. We formulate the problem as a
single-player game, which we call the mallocGame, such that high-reward
trajectories of the game correspond to efficient memory mappings on the target
hardware. We also introduce a Reinforcement Learning agent, mallocMuZero, and
show that it is capable of playing this game to discover new and improved
memory mapping solutions that lead to faster execution times on real ML
workloads on ML accelerators. We compare the performance of mallocMuZero to the
default solver used by the Accelerated Linear Algebra (XLA) compiler on a
benchmark of realistic ML workloads. In addition, we show that mallocMuZero is
capable of improving the execution time of the recently published AlphaTensor
matrix multiplication model.
Towards best practices in AGI safety and governance: A survey of expert
  opinion
Authors: name: Jonas Schuett, name: Noemi Dreksler, name: Markus Anderljung, name: David McCaffary, name: Lennart Heim, name: Emma Bluemke, name: Ben Garfinkel
Published: 2023-05-11 21:54:30

A number of leading AI companies, including OpenAI, Google DeepMind, and
Anthropic, have the stated goal of building artificial general intelligence
(AGI) - AI systems that achieve or exceed human performance across a wide range
of cognitive tasks. In pursuing this goal, they may develop and deploy AI
systems that pose particularly significant risks. While they have already taken
some measures to mitigate these risks, best practices have not yet emerged. To
support the identification of best practices, we sent a survey to 92 leading
experts from AGI labs, academia, and civil society and received 51 responses.
Participants were asked how much they agreed with 50 statements about what AGI
labs should do. Our main finding is that participants, on average, agreed with
all of them. Many statements received extremely high levels of agreement. For
example, 98% of respondents somewhat or strongly agreed that AGI labs should
conduct pre-deployment risk assessments, dangerous capabilities evaluations,
third-party model audits, safety restrictions on model usage, and red teaming.
Ultimately, our list of statements may serve as a helpful foundation for
efforts to develop best practices, standards, and regulations for AGI labs.
Measuring Progress in Fine-grained Vision-and-Language Understanding
Authors: name: Emanuele Bugliarello, name: Laurent Sartran, name: Aishwarya Agrawal, name: Lisa Anne Hendricks, name: Aida Nematzadeh
Published: 2023-05-12 15:34:20

While pretraining on large-scale image-text data from the Web has facilitated
rapid progress on many vision-and-language (V&L) tasks, recent work has
demonstrated that pretrained models lack "fine-grained" understanding, such as
the ability to recognise relationships, verbs, and numbers in images. This has
resulted in an increased interest in the community to either develop new
benchmarks or models for such capabilities. To better understand and quantify
progress in this direction, we investigate four competitive V&L models on four
fine-grained benchmarks. Through our analysis, we find that X-VLM (Zeng et al.,
2022) consistently outperforms other baselines, and that modelling innovations
can impact performance more than scaling Web data, which even degrades
performance sometimes. Through a deeper investigation of X-VLM, we highlight
the importance of both novel losses and rich data sources for learning
fine-grained skills. Finally, we inspect training dynamics, and discover that
for some tasks, performance peaks early in training or significantly
fluctuates, never converging.
What are the Desired Characteristics of Calibration Sets? Identifying
  Correlates on Long Form Scientific Summarization
Authors: name: Griffin Adams, name: Bichlien H Nguyen, name: Jake Smith, name: Yingce Xia, name: Shufang Xie, name: Anna Ostropolets, name: Budhaditya Deb, name: Yuan-Jyue Chen, name: Tristan Naumann, name: Noémie Elhadad
Published: 2023-05-12 17:08:47

Summarization models often generate text that is poorly calibrated to quality
metrics because they are trained to maximize the likelihood of a single
reference (MLE). To address this, recent work has added a calibration step,
which exposes a model to its own ranked outputs to improve relevance or, in a
separate line of work, contrasts positive and negative sets to improve
faithfulness. While effective, much of this work has focused on how to generate
and optimize these sets. Less is known about why one setup is more effective
than another. In this work, we uncover the underlying characteristics of
effective sets. For each training instance, we form a large, diverse pool of
candidates and systematically vary the subsets used for calibration
fine-tuning. Each selection strategy targets distinct aspects of the sets, such
as lexical diversity or the size of the gap between positive and negatives. On
three diverse scientific long-form summarization datasets (spanning biomedical,
clinical, and chemical domains), we find, among others, that faithfulness
calibration is optimal when the negative sets are extractive and more likely to
be generated, whereas for relevance calibration, the metric margin between
candidates should be maximized and surprise--the disagreement between model and
metric defined candidate rankings--minimized. Code to create, select, and
optimize calibration sets is available at
https://github.com/griff4692/calibrating-summaries
BlendFields: Few-Shot Example-Driven Facial Modeling
Authors: name: Kacper Kania, name: Stephan J. Garbin, name: Andrea Tagliasacchi, name: Virginia Estellers, name: Kwang Moo Yi, name: Julien Valentin, name: Tomasz Trzciński, name: Marek Kowalski
Published: 2023-05-12 14:30:07

Generating faithful visualizations of human faces requires capturing both
coarse and fine-level details of the face geometry and appearance. Existing
methods are either data-driven, requiring an extensive corpus of data not
publicly accessible to the research community, or fail to capture fine details
because they rely on geometric face models that cannot represent fine-grained
details in texture with a mesh discretization and linear deformation designed
to model only a coarse face geometry. We introduce a method that bridges this
gap by drawing inspiration from traditional computer graphics techniques.
Unseen expressions are modeled by blending appearance from a sparse set of
extreme poses. This blending is performed by measuring local volumetric changes
in those expressions and locally reproducing their appearance whenever a
similar expression is performed at test time. We show that our method
generalizes to unseen expressions, adding fine-grained effects on top of smooth
volumetric deformations of a face, and demonstrate how it generalizes beyond
faces.