Orionnnn/hub-stats huggingface dataset:
Thread of Thought Unraveling Chaotic Contexts
Authors: name: Yucheng Zhou, name: Xiubo Geng, name: Tao Shen, name: Chongyang Tao, name: Guodong Long, name: Jian-Guang Lou, name: Jianbing Shen
Published: 2023-11-15 06:54:44

Large Language Models (LLMs) have ushered in a transformative era in the
field of natural language processing, excelling in tasks related to text
comprehension and generation. Nevertheless, they encounter difficulties when
confronted with chaotic contexts (e.g., distractors rather than long irrelevant
context), leading to the inadvertent omission of certain details within the
chaotic context. In response to these challenges, we introduce the "Thread of
Thought" (ThoT) strategy, which draws inspiration from human cognitive
processes. ThoT systematically segments and analyzes extended contexts while
adeptly selecting pertinent information. This strategy serves as a versatile
"plug-and-play" module, seamlessly integrating with various LLMs and prompting
techniques. In the experiments, we utilize the PopQA and EntityQ datasets, as
well as a Multi-Turn Conversation Response dataset (MTCR) we collected, to
illustrate that ThoT significantly improves reasoning performance compared to
other prompting techniques.
Routing to the Expert: Efficient Reward-guided Ensemble of Large
  Language Models
Authors: name: Keming Lu, name: Hongyi Yuan, name: Runji Lin, name: Junyang Lin, name: Zheng Yuan, name: Chang Zhou, name: Jingren Zhou
Published: 2023-11-15 04:40:43

The complementary potential of Large Language Models (LLM) assumes
off-the-shelf LLMs have heterogeneous expertise in a wide range of domains and
tasks so that an ensemble of LLMs can achieve consistently better performance.
Existing ensemble methods for LLMs mainly focus on reward model ranking of
outputs, leading to significant computation overhead. To combat this issue, we
revisit the complementary potential of LLMs and further elaborate it by mining
latent expertise with off-the-shelf reward models. We propose Zooter, a
reward-guided routing method distilling rewards on training queries to train a
routing function, which can precisely distribute each query to the LLM with
expertise about it. We also integrate a tag-based label enhancement to mitigate
noise from uncertainty when using rewards as silver supervision. Zooter shows
computation efficiency in inference as it introduces only a minor computation
overhead of a routing function compared with reward model ranking methods. We
evaluate Zooter on a comprehensive benchmark collection with 26 subsets on
different domains and tasks. Zooter outperforms the best single model on
average and ranks first on 44% of tasks, even surpassing multiple reward model
ranking methods.
UT5: Pretraining Non autoregressive T5 with unrolled denoising
Authors: name: Mahmoud G. Salem, name: Jiayu Ye, name: Chu-Cheng Lin, name: Frederick Liu
Published: 2023-11-14 21:28:10

Recent advances in Transformer-based Large Language Models have made great
strides in natural language generation. However, to decode K tokens, an
autoregressive model needs K sequential forward passes, which may be a
performance bottleneck for large language models. Many non-autoregressive (NAR)
research are aiming to address this sequentiality bottleneck, albeit many have
focused on a dedicated architecture in supervised benchmarks. In this work, we
studied unsupervised pretraining for non auto-regressive T5 models via unrolled
denoising and shown its SoTA results in downstream generation tasks such as
SQuAD question generation and XSum.
UNcommonsense Reasoning: Abductive Reasoning about Uncommon Situations
Authors: name: Wenting Zhao, name: Justin T Chiu, name: Jena D. Hwang, name: Faeze Brahman, name: Jack Hessel, name: Sanjiban Choudhury, name: Yejin Choi, name: Xiang Lorraine Li, name: Alane Suhr
Published: 2023-11-14 19:00:55

Language technologies that accurately model the dynamics of events must
perform commonsense reasoning. Existing work evaluating commonsense reasoning
focuses on making inferences about common, everyday situations. To instead
investigate the ability to model unusual, unexpected, and unlikely situations,
we explore the task of uncommonsense abductive reasoning. Given a piece of
context with an unexpected outcome, this task requires reasoning abductively to
generate a natural language explanation that makes the unexpected outcome more
likely in the context. To this end, we curate and release a new English
language corpus called UNcommonsense. We characterize the differences between
the performance of human explainers and the best performing large language
models, finding that model-enhanced human-written explanations achieve the
highest quality by trading off between specificity and diversity. Finally, we
experiment with several online imitation learning algorithms to train open and
accessible language models on this task. When compared with the vanilla
supervised fine-tuning approach, these methods consistently reduce lose rates
on both common and uncommonsense abductive reasoning judged by human
evaluators.
GRIM: GRaph-based Interactive narrative visualization for gaMes
Authors: name: Jorge Leandro, name: Sudha Rao, name: Michael Xu, name: Weijia Xu, name: Nebosja Jojic, name: Chris Brockett, name: Bill Dolan
Published: 2023-11-15 18:55:45

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The
narratives of these may take years to write and typically involve a large
creative team. In this work, we demonstrate the potential of large generative
text models to assist this process. GRIM, a prototype
GRaph-based Interactive narrative visualization system for
gaMes, generates a rich narrative graph with branching storylines that
match a high-level narrative description and constraints provided by the
designer. Game designers can interactively edit the graph by automatically
generating new sub-graphs that fit the edits within the original narrative and
constraints. We illustrate the use of GRIM in conjunction with GPT-4,
generating branching narratives for four well-known stories with different
contextual constraints.
Fast Chain-of-Thought: A Glance of Future from Parallel Decoding Leads
  to Answers Faster
Authors: name: Hongxuan Zhang, name: Zhining Liu, name: Jiaqi Zheng, name: Chenyi Zhuang, name: Jinjie Gu, name: Guihai Chen
Published: 2023-11-14 15:56:18

In this work, we propose FastCoT, a model-agnostic framework based on
parallel decoding without any further training of an auxiliary model or
modification to the LLM itself. FastCoT uses a size-varying context window
whose size changes with position to conduct parallel decoding and
auto-regressive decoding simultaneously, thus fully utilizing GPU computation
resources. In FastCoT, the parallel decoding part provides the LLM with a quick
glance of the future composed of approximate tokens, which could lead to faster
answers compared to regular autoregressive decoding used by causal
transformers. We also provide an implementation of parallel decoding within
LLM, which supports KV-cache generation and batch processing. Through extensive
experiments, we demonstrate that FastCoT saves inference time by nearly 20%
with only a negligible performance drop compared to the regular approach.
Additionally, we show that the context window size exhibits considerable
robustness for different tasks.
Technical Report: Large Language Models can Strategically Deceive their
  Users when Put Under Pressure
Authors: name: Jérémy Scheurer, name: Mikita Balesni, name: Marius Hobbhahn
Published: 2023-11-09 17:12:44

We demonstrate a situation in which Large Language Models, trained to be
helpful, harmless, and honest, can display misaligned behavior and
strategically deceive their users about this behavior without being instructed
to do so. Concretely, we deploy GPT-4 as an agent in a realistic, simulated
environment, where it assumes the role of an autonomous stock trading agent.
Within this environment, the model obtains an insider tip about a lucrative
stock trade and acts upon it despite knowing that insider trading is
disapproved of by company management. When reporting to its manager, the model
consistently hides the genuine reasons behind its trading decision. We perform
a brief investigation of how this behavior varies under changes to the setting,
such as removing model access to a reasoning scratchpad, attempting to prevent
the misaligned behavior by changing system instructions, changing the amount of
pressure the model is under, varying the perceived risk of getting caught, and
making other simple changes to the environment. To our knowledge, this is the
first demonstration of Large Language Models trained to be helpful, harmless,
and honest, strategically deceiving their users in a realistic situation
without direct instructions or training for deception.
Instant3D: Instant Text-to-3D Generation
Authors: name: Ming Li, name: Pan Zhou, name: Jia-Wei Liu, name: Jussi Keppo, name: Min Lin, name: Shuicheng Yan, name: Xiangyu Xu
Published: 2023-11-14 18:59:59

Text-to-3D generation, which aims to synthesize vivid 3D objects from text
prompts, has attracted much attention from the computer vision community. While
several existing works have achieved impressive results for this task, they
mainly rely on a time-consuming optimization paradigm. Specifically, these
methods optimize a neural field from scratch for each text prompt, taking
approximately one hour or more to generate one object. This heavy and
repetitive training cost impedes their practical deployment. In this paper, we
propose a novel framework for fast text-to-3D generation, dubbed Instant3D.
Once trained, Instant3D is able to create a 3D object for an unseen text prompt
in less than one second with a single run of a feedforward network. We achieve
this remarkable speed by devising a new network that directly constructs a 3D
triplane from a text prompt. The core innovation of our Instant3D lies in our
exploration of strategies to effectively inject text conditions into the
network. Furthermore, we propose a simple yet effective activation function,
the scaled-sigmoid, to replace the original sigmoid function, which speeds up
the training convergence by more than ten times. Finally, to address the Janus
(multi-head) problem in 3D generation, we propose an adaptive Perp-Neg
algorithm that can dynamically adjust its concept negation scales according to
the severity of the Janus problem during training, effectively reducing the
multi-head effect. Extensive experiments on a wide variety of benchmark
datasets demonstrate that the proposed algorithm performs favorably against the
state-of-the-art methods both qualitatively and quantitatively, while achieving
significantly better efficiency. The project page is at
https://ming1993li.github.io/Instant3DProj.
One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View
  Generation and 3D Diffusion
Authors: name: Minghua Liu, name: Ruoxi Shi, name: Linghao Chen, name: Zhuoyang Zhang, name: Chao Xu, name: Xinyue Wei, name: Hansheng Chen, name: Chong Zeng, name: Jiayuan Gu, name: Hao Su
Published: 2023-11-14 03:40:25

Recent advancements in open-world 3D object generation have been remarkable,
with image-to-3D methods offering superior fine-grained control over their
text-to-3D counterparts. However, most existing models fall short in
simultaneously providing rapid generation speeds and high fidelity to input
images - two features essential for practical applications. In this paper, we
present One-2-3-45++, an innovative method that transforms a single image into
a detailed 3D textured mesh in approximately one minute. Our approach aims to
fully harness the extensive knowledge embedded in 2D diffusion models and
priors from valuable yet limited 3D data. This is achieved by initially
finetuning a 2D diffusion model for consistent multi-view image generation,
followed by elevating these images to 3D with the aid of multi-view conditioned
3D native diffusion models. Extensive experimental evaluations demonstrate that
our method can produce high-quality, diverse 3D assets that closely mirror the
original input image. Our project webpage:
https://sudo-ai-3d.github.io/One2345plus_page.
Qwen-Audio: Advancing Universal Audio Understanding via Unified
  Large-Scale Audio-Language Models
Authors: name: Yunfei Chu, name: Jin Xu, name: Xiaohuan Zhou, name: Qian Yang, name: Shiliang Zhang, name: Zhijie Yan, name: Chang Zhou, name: Jingren Zhou
Published: 2023-11-14 05:34:50

Recently, instruction-following audio-language models have received broad
attention for audio interaction with humans. However, the absence of
pre-trained audio models capable of handling diverse audio types and tasks has
hindered progress in this field. Consequently, most existing works have only
been able to support a limited range of interaction capabilities. In this
paper, we develop the Qwen-Audio model and address this limitation by scaling
up audio-language pre-training to cover over 30 tasks and various audio types,
such as human speech, natural sounds, music, and songs, to facilitate universal
audio understanding abilities. However, directly co-training all tasks and
datasets can lead to interference issues, as the textual labels associated with
different datasets exhibit considerable variations due to differences in task
focus, language, granularity of annotation, and text structure. To overcome the
one-to-many interference, we carefully design a multi-task training framework
by conditioning on a sequence of hierarchical tags to the decoder for
encouraging knowledge sharing and avoiding interference through shared and
specified tags respectively. Remarkably, Qwen-Audio achieves impressive
performance across diverse benchmark tasks without requiring any task-specific
fine-tuning, surpassing its counterparts. Building upon the capabilities of
Qwen-Audio, we further develop Qwen-Audio-Chat, which allows for input from
various audios and text inputs, enabling multi-turn dialogues and supporting
various audio-central scenarios.
Fine-tuning Language Models for Factuality
Authors: name: Katherine Tian, name: Eric Mitchell, name: Huaxiu Yao, name: Christopher D. Manning, name: Chelsea Finn
Published: 2023-11-14 18:59:15

The fluency and creativity of large pre-trained language models (LLMs) have
led to their widespread use, sometimes even as a replacement for traditional
search engines. Yet language models are prone to making convincing but
factually inaccurate claims, often referred to as 'hallucinations.' These
errors can inadvertently spread misinformation or harmfully perpetuate
misconceptions. Further, manual fact-checking of model responses is a
time-consuming process, making human factuality labels expensive to acquire. In
this work, we fine-tune language models to be more factual, without human
labeling and targeting more open-ended generation settings than past work. We
leverage two key recent innovations in NLP to do so. First, several recent
works have proposed methods for judging the factuality of open-ended text by
measuring consistency with an external knowledge base or simply a large model's
confidence scores. Second, the direct preference optimization algorithm enables
straightforward fine-tuning of language models on objectives other than
supervised imitation, using a preference ranking over possible model responses.
We show that learning from automatically generated factuality preference
rankings, generated either through existing retrieval systems or our novel
retrieval-free approach, significantly improves the factuality (percent of
generated claims that are correct) of Llama-2 on held-out topics compared with
RLHF or decoding strategies targeted at factuality. At 7B scale, compared to
Llama-2-chat, we observe 58% and 40% reduction in factual error rate when
generating biographies and answering medical questions, respectively.
DiLoCo: Distributed Low-Communication Training of Language Models
Authors: name: Arthur Douillard, name: Qixuan Feng, name: Andrei A. Rusu, name: Rachita Chhaparia, name: Yani Donchev, name: Adhiguna Kuncoro, name: Marc'Aurelio Ranzato, name: Arthur Szlam, name: Jiajun Shen
Published: 2023-11-14 12:05:45

Large language models (LLM) have become a critical component in many
applications of machine learning. However, standard approaches to training LLM
require a large number of tightly interconnected accelerators, with devices
exchanging gradients and other intermediate states at each optimization step.
While it is difficult to build and maintain a single computing cluster hosting
many accelerators, it might be easier to find several computing clusters each
hosting a smaller number of devices. In this work, we propose a distributed
optimization algorithm, Distributed Low-Communication (DiLoCo), that enables
training of language models on islands of devices that are poorly connected.
The approach is a variant of federated averaging, where the number of inner
steps is large, the inner optimizer is AdamW, and the outer optimizer is
Nesterov momentum. On the widely used C4 dataset, we show that DiLoCo on 8
workers performs as well as fully synchronous optimization while communicating
500 times less. DiLoCo exhibits great robustness to the data distribution of
each worker. It is also robust to resources becoming unavailable over time, and
vice versa, it can seamlessly leverage resources that become available during
training.
Unifying the Perspectives of NLP and Software Engineering: A Survey on
  Language Models for Code
Authors: name: Ziyin Zhang, name: Chaoyu Chen, name: Bingchang Liu, name: Cong Liao, name: Zi Gong, name: Hang Yu, name: Jianguo Li, name: Rui Wang
Published: 2023-11-14 08:34:26

In this work we systematically review the recent advancements in software
engineering with language models, covering 70+ models, 40+ evaluation tasks,
180+ datasets, and 900 related works. Unlike previous works, we integrate
software engineering (SE) with natural language processing (NLP) by discussing
the perspectives of both sides: SE applies language models for development
automation, while NLP adopts SE tasks for language model evaluation. We break
down code processing models into general language models represented by the GPT
family and specialized models that are specifically pretrained on code, often
with tailored objectives. We discuss the relations and differences between
these models, and highlight the historical transition of code modeling from
statistical models and RNNs to pretrained Transformers and LLMs, which is
exactly the same course that had been taken by NLP. We also go beyond
programming and review LLMs' application in other software engineering
activities including requirement engineering, testing, deployment, and
operations in an endeavor to provide a global view of NLP in SE, and identify
key challenges and potential future directions in this domain. We keep the
survey open and updated on GitHub at
https://github.com/codefuse-ai/Awesome-Code-LLM.
The ART of LLM Refinement: Ask, Refine, and Trust
Authors: name: Kumar Shridhar, name: Koustuv Sinha, name: Andrew Cohen, name: Tianlu Wang, name: Ping Yu, name: Ram Pasunuru, name: Mrinmaya Sachan, name: Jason Weston, name: Asli Celikyilmaz
Published: 2023-11-14 07:26:32

In recent years, Large Language Models (LLMs) have demonstrated remarkable
generative abilities, but can they judge the quality of their own generations?
A popular concept, referred to as self-refinement, postulates that LLMs can
detect and correct the errors in their generations when asked to do so.
However, recent empirical evidence points in the opposite direction, suggesting
that LLMs often struggle to accurately identify errors when reasoning is
involved. To address this, we propose a reasoning with refinement objective
called ART: Ask, Refine, and Trust, which asks necessary questions to decide
when an LLM should refine its output, and either affirm or withhold trust in
its refinement by ranking the refinement and the initial prediction. On two
multistep reasoning tasks of mathematical word problems (GSM8K) and question
answering (StrategyQA), ART achieves a performance gain of +5 points over
self-refinement baselines, while using a much smaller model as the decision
maker. We also demonstrate the benefit of using smaller models to make
refinement decisions as a cost-effective alternative to fine-tuning a larger
model.
Instruction-Following Evaluation for Large Language Models
Authors: name: Jeffrey Zhou, name: Tianjian Lu, name: Swaroop Mishra, name: Siddhartha Brahma, name: Sujoy Basu, name: Yi Luan, name: Denny Zhou, name: Le Hou
Published: 2023-11-14 05:13:55

One core capability of Large Language Models (LLMs) is to follow natural
language instructions. However, the evaluation of such abilities is not
standardized: Human evaluations are expensive, slow, and not objectively
reproducible, while LLM-based auto-evaluation is potentially biased or limited
by the ability of the evaluator LLM. To overcome these issues, we introduce
Instruction-Following Eval (IFEval) for large language models. IFEval is a
straightforward and easy-to-reproduce evaluation benchmark. It focuses on a set
of "verifiable instructions" such as "write in more than 400 words" and
"mention the keyword of AI at least 3 times". We identified 25 types of those
verifiable instructions and constructed around 500 prompts, with each prompt
containing one or more verifiable instructions. We show evaluation results of
two widely available LLMs on the market. Our code and data can be found at
https://github.com/google-research/google-research/tree/master/instruction_following_eval
MART: Improving LLM Safety with Multi-round Automatic Red-Teaming
Authors: name: Suyu Ge, name: Chunting Zhou, name: Rui Hou, name: Madian Khabsa, name: Yi-Chia Wang, name: Qifan Wang, name: Jiawei Han, name: Yuning Mao
Published: 2023-11-13 19:13:29

Red-teaming is a common practice for mitigating unsafe behaviors in Large
Language Models (LLMs), which involves thoroughly assessing LLMs to identify
potential flaws and addressing them with responsible and accurate responses.
While effective, manual red-teaming is costly, and existing automatic
red-teaming typically discovers safety risks without addressing them. In this
paper, we propose a Multi-round Automatic Red-Teaming (MART) method, which
incorporates both automatic adversarial prompt writing and safe response
generation, significantly increasing red-teaming scalability and the safety of
the target LLM. Specifically, an adversarial LLM and a target LLM interplay
with each other in an iterative manner, where the adversarial LLM aims to
generate challenging prompts that elicit unsafe responses from the target LLM,
while the target LLM is fine-tuned with safety aligned data on these
adversarial prompts. In each round, the adversarial LLM crafts better attacks
on the updated target LLM, while the target LLM also improves itself through
safety fine-tuning. On adversarial prompt benchmarks, the violation rate of an
LLM with limited safety alignment reduces up to 84.7% after 4 rounds of MART,
achieving comparable performance to LLMs with extensive adversarial prompt
writing. Notably, model helpfulness on non-adversarial prompts remains stable
throughout iterations, indicating the target LLM maintains strong performance
on instruction following.