Orionnnn/hub-stats huggingface dataset:
Collective Critics for Creative Story Generation
Authors: name: Minwook Bae, name: Hyounghun Kim
Published: 2024-10-03 12:21:17

Generating a long story of several thousand words with narrative coherence
using Large Language Models (LLMs) has been a challenging task. Previous
research has addressed this challenge by proposing different frameworks that
create a story plan and generate a long story based on that plan. However,
these frameworks have been mainly focusing on maintaining narrative coherence
in stories, often overlooking creativity in story planning and the
expressiveness of the stories generated from those plans, which are desirable
properties to captivate readers' interest. In this paper, we propose Collective
Critics for Creative Story Generation framework (CritiCS), which is composed of
plan refining stage (CrPlan) and story generation stage (CrText), to integrate
a collective revision mechanism that promotes those properties into long-form
story generation process. Specifically, in each stage, a group of LLM critics
and one leader collaborate to incrementally refine drafts of plan and story
throughout multiple rounds. Extensive human evaluation shows that the CritiCS
can significantly enhance story creativity and reader engagement, while also
maintaining narrative coherence. Furthermore, the design of the framework
allows active participation from human writers in any role within the critique
process, enabling interactive human-machine collaboration in story writing.
Response Tuning: Aligning Large Language Models without Instruction
Authors: name: Seokhyun An, name: Hyounghun Kim
Published: 2024-10-03 13:15:19

Instruction tuning-supervised fine-tuning using instruction-response pairs-is
a foundational step in transitioning pre-trained Large Language Models (LLMs)
into helpful and safe chat assistants. Our hypothesis is that establishing an
adequate output space can enable such a transition given the capabilities
inherent in pre-trained LLMs. To verify this, we propose Response Tuning (RT),
which eliminates the instruction-conditioning step in instruction tuning and
solely focuses on response space supervision. Our experiments demonstrate that
RT models, trained only using responses, can effectively respond to a wide
range of instructions and exhibit helpfulness comparable to that of their
instruction-tuned counterparts. Furthermore, we observe that controlling the
training response distribution can significantly improve their user preference
or elicit target behaviors such as refusing assistance for unsafe queries. Our
findings illuminate the role of establishing an adequate output space in
alignment, highlighting the potential of the extensive inherent capabilities of
pre-trained LLMs.
Mixed-Session Conversation with Egocentric Memory
Authors: name: Jihyoung Jang, name: Taeyoung Kim, name: Hyounghun Kim
Published: 2024-10-03 14:06:43

Recently introduced dialogue systems have demonstrated high usability.
However, they still fall short of reflecting real-world conversation scenarios.
Current dialogue systems exhibit an inability to replicate the dynamic,
continuous, long-term interactions involving multiple partners. This shortfall
arises because there have been limited efforts to account for both aspects of
real-world dialogues: deeply layered interactions over the long-term dialogue
and widely expanded conversation networks involving multiple participants. As
the effort to incorporate these aspects combined, we introduce Mixed-Session
Conversation, a dialogue system designed to construct conversations with
various partners in a multi-session dialogue setup. We propose a new dataset
called MiSC to implement this system. The dialogue episodes of MiSC consist of
6 consecutive sessions, with four speakers (one main speaker and three
partners) appearing in each episode. Also, we propose a new dialogue model with
a novel memory management mechanism, called Egocentric Memory Enhanced
Mixed-Session Conversation Agent (EMMA). EMMA collects and retains memories
from the main speaker's perspective during conversations with partners,
enabling seamless continuity in subsequent interactions. Extensive human
evaluations validate that the dialogues in MiSC demonstrate a seamless
conversational flow, even when conversation partners change in each session.
EMMA trained with MiSC is also evaluated to maintain high memorability without
contradiction throughout the entire conversation.
Diversity-Rewarded CFG Distillation
Authors: name: Geoffrey Cideron, name: Andrea Agostinelli, name: Johan Ferret, name: Sertan Girgin, name: Romuald Elie, name: Olivier Bachem, name: Sarah Perrin, name: Alexandre Ramé
Published: 2024-10-08 14:40:51

Generative models are transforming creative domains such as music generation,
with inference-time strategies like Classifier-Free Guidance (CFG) playing a
crucial role. However, CFG doubles inference cost while limiting originality
and diversity across generated contents. In this paper, we introduce
diversity-rewarded CFG distillation, a novel finetuning procedure that distills
the strengths of CFG while addressing its limitations. Our approach optimises
two training objectives: (1) a distillation objective, encouraging the model
alone (without CFG) to imitate the CFG-augmented predictions, and (2) an RL
objective with a diversity reward, promoting the generation of diverse outputs
for a given prompt. By finetuning, we learn model weights with the ability to
generate high-quality and diverse outputs, without any inference overhead. This
also unlocks the potential of weight-based model merging strategies: by
interpolating between the weights of two models (the first focusing on quality,
the second on diversity), we can control the quality-diversity trade-off at
deployment time, and even further boost performance. We conduct extensive
experiments on the MusicLM (Agostinelli et al., 2023) text-to-music generative
model, where our approach surpasses CFG in terms of quality-diversity Pareto
optimality. According to human evaluators, our finetuned-then-merged model
generates samples with higher quality-diversity than the base model augmented
with CFG. Explore our generations at
https://google-research.github.io/seanet/musiclm/diverse_music/.
Jointly Generating Multi-view Consistent PBR Textures using
  Collaborative Control
Authors: name: Shimon Vainer, name: Konstantin Kutsy, name: Dante De Nigris, name: Ciara Rowles, name: Slava Elizarov, name: Simon Donné
Published: 2024-10-09 15:21:46

Multi-view consistency remains a challenge for image diffusion models. Even
within the Text-to-Texture problem, where perfect geometric correspondences are
known a priori, many methods fail to yield aligned predictions across views,
necessitating non-trivial fusion methods to incorporate the results onto the
original mesh. We explore this issue for a Collaborative Control workflow
specifically in PBR Text-to-Texture. Collaborative Control directly models PBR
image probability distributions, including normal bump maps; to our knowledge,
the only diffusion model to directly output full PBR stacks. We discuss the
design decisions involved in making this model multi-view consistent, and
demonstrate the effectiveness of our approach in ablation studies, as well as
practical applications.
ING-VP: MLLMs cannot Play Easy Vision-based Games Yet
Authors: name: Haoran Zhang, name: Hangyu Guo, name: Shuyue Guo, name: Meng Cao, name: Wenhao Huang, name: Jiaheng Liu, name: Ge Zhang
Published: 2024-10-09 05:17:38

As multimodal large language models (MLLMs) continue to demonstrate
increasingly competitive performance across a broad spectrum of tasks, more
intricate and comprehensive benchmarks have been developed to assess these
cutting-edge models. These benchmarks introduce new challenges to core
capabilities such as perception, reasoning, and planning. However, existing
multimodal benchmarks fall short in providing a focused evaluation of
multi-step planning based on spatial relationships in images. To bridge this
gap, we present ING-VP, the first INteractive Game-based Vision Planning
benchmark, specifically designed to evaluate the spatial imagination and
multi-step reasoning abilities of MLLMs. ING-VP features 6 distinct games,
encompassing 300 levels, each with 6 unique configurations. A single model
engages in over 60,000 rounds of interaction. The benchmark framework allows
for multiple comparison settings, including image-text vs. text-only inputs,
single-step vs. multi-step reasoning, and with-history vs. without-history
conditions, offering valuable insights into the model's capabilities. We
evaluated numerous state-of-the-art MLLMs, with the highest-performing model,
Claude-3.5 Sonnet, achieving an average accuracy of only 3.37%, far below the
anticipated standard. This work aims to provide a specialized evaluation
framework to drive advancements in MLLMs' capacity for complex spatial
reasoning and planning. The code is publicly available at
https://github.com/Thisisus7/ING-VP.git.
GLEE: A Unified Framework and Benchmark for Language-based Economic
  Environments
Authors: name: Eilam Shapira, name: Omer Madmon, name: Itamar Reinman, name: Samuel Joseph Amouyal, name: Roi Reichart, name: Moshe Tennenholtz
Published: 2024-10-07 17:55:35

Large Language Models (LLMs) show significant potential in economic and
strategic interactions, where communication via natural language is often
prevalent. This raises key questions: Do LLMs behave rationally? Can they mimic
human behavior? Do they tend to reach an efficient and fair outcome? What is
the role of natural language in the strategic interaction? How do
characteristics of the economic environment influence these dynamics? These
questions become crucial concerning the economic and societal implications of
integrating LLM-based agents into real-world data-driven systems, such as
online retail platforms and recommender systems. While the ML community has
been exploring the potential of LLMs in such multi-agent setups, varying
assumptions, design choices and evaluation criteria across studies make it
difficult to draw robust and meaningful conclusions. To address this, we
introduce a benchmark for standardizing research on two-player, sequential,
language-based games. Inspired by the economic literature, we define three base
families of games with consistent parameterization, degrees of freedom and
economic measures to evaluate agents' performance (self-gain), as well as the
game outcome (efficiency and fairness). We develop an open-source framework for
interaction simulation and analysis, and utilize it to collect a dataset of LLM
vs. LLM interactions across numerous game configurations and an additional
dataset of human vs. LLM interactions. Through extensive experimentation, we
demonstrate how our framework and dataset can be used to: (i) compare the
behavior of LLM-based agents to human players in various economic contexts;
(ii) evaluate agents in both individual and collective performance measures;
and (iii) quantify the effect of the economic characteristics of the
environments on the behavior of agents.
TinyEmo: Scaling down Emotional Reasoning via Metric Projection
Authors: name: Cristian Gutierrez
Published: 2024-10-09 17:03:49

This paper introduces TinyEmo, a family of small multi-modal language models
for emotional reasoning and classification. Our approach features: (1) a
synthetic emotional instruct dataset for both pre-training and fine-tuning
stages, (2) a Metric Projector that delegates classification from the language
model allowing for more efficient training and inference, (3) a multi-modal
large language model (MM-LLM) for emotional reasoning, and (4) a semi-automated
framework for bias detection. TinyEmo is able to perform emotion classification
and emotional reasoning, all while using substantially fewer parameters than
comparable models. This efficiency allows us to freely incorporate more diverse
emotional datasets, enabling strong performance on classification tasks, with
our smallest model (700M parameters) outperforming larger state-of-the-art
models based on general-purpose MM-LLMs with over 7B parameters. Additionally,
the Metric Projector allows for interpretability and indirect bias detection in
large models without additional training, offering an approach to understand
and improve AI systems.
  We release code, models, and dataset at https://github.com/ggcr/TinyEmo
Falcon Mamba: The First Competitive Attention-free 7B Language Model
Authors: name: Jingwei Zuo, name: Maksim Velikanov, name: Dhia Eddine Rhaiem, name: Ilyas Chahed, name: Younes Belkada, name: Guillaume Kunsch, name: Hakim Hacid
Published: 2024-10-07 15:40:45

In this technical report, we present Falcon Mamba 7B, a new base large
language model based on the novel Mamba architecture. Falcon Mamba 7B is
trained on 5.8 trillion tokens with carefully selected data mixtures. As a pure
Mamba-based model, Falcon Mamba 7B surpasses leading open-weight models based
on Transformers, such as Mistral 7B, Llama3.1 8B, and Falcon2 11B. It is on par
with Gemma 7B and outperforms models with different architecture designs, such
as RecurrentGemma 9B and RWKV-v6 Finch 7B/14B. Currently, Falcon Mamba 7B is
the best-performing Mamba model in the literature at this scale, surpassing
both existing Mamba and hybrid Mamba-Transformer models, according to the Open
LLM Leaderboard. Due to its architecture, Falcon Mamba 7B is significantly
faster at inference and requires substantially less memory for long sequence
generation. Despite recent studies suggesting that hybrid Mamba-Transformer
models outperform pure architecture designs, we demonstrate that even the pure
Mamba design can achieve similar, or even superior results compared to the
Transformer and hybrid designs. We make the weights of our implementation of
Falcon Mamba 7B publicly available on
https://huggingface.co/tiiuae/falcon-mamba-7b, under a permissive license.
Retrieval-Augmented Decision Transformer: External Memory for In-context
  RL
Authors: name: Thomas Schmied, name: Fabian Paischer, name: Vihang Patil, name: Markus Hofmarcher, name: Razvan Pascanu, name: Sepp Hochreiter
Published: 2024-10-09 17:15:30

In-context learning (ICL) is the ability of a model to learn a new task by
observing a few exemplars in its context. While prevalent in NLP, this
capability has recently also been observed in Reinforcement Learning (RL)
settings. Prior in-context RL methods, however, require entire episodes in the
agent's context. Given that complex environments typically lead to long
episodes with sparse rewards, these methods are constrained to simple
environments with short episodes. To address these challenges, we introduce
Retrieval-Augmented Decision Transformer (RA-DT). RA-DT employs an external
memory mechanism to store past experiences from which it retrieves only
sub-trajectories relevant for the current situation. The retrieval component in
RA-DT does not require training and can be entirely domain-agnostic. We
evaluate the capabilities of RA-DT on grid-world environments, robotics
simulations, and procedurally-generated video games. On grid-worlds, RA-DT
outperforms baselines, while using only a fraction of their context length.
Furthermore, we illuminate the limitations of current in-context RL methods on
complex environments and discuss future directions. To facilitate future
research, we release datasets for four of the considered environments.
F5-TTS: A Fairytaler that Fakes Fluent and Faithful Speech with Flow
  Matching
Authors: name: Yushen Chen, name: Zhikang Niu, name: Ziyang Ma, name: Keqi Deng, name: Chunhui Wang, name: Jian Zhao, name: Kai Yu, name: Xie Chen
Published: 2024-10-09 13:46:34

This paper introduces F5-TTS, a fully non-autoregressive text-to-speech
system based on flow matching with Diffusion Transformer (DiT). Without
requiring complex designs such as duration model, text encoder, and phoneme
alignment, the text input is simply padded with filler tokens to the same
length as input speech, and then the denoising is performed for speech
generation, which was originally proved feasible by E2 TTS. However, the
original design of E2 TTS makes it hard to follow due to its slow convergence
and low robustness. To address these issues, we first model the input with
ConvNeXt to refine the text representation, making it easy to align with the
speech. We further propose an inference-time Sway Sampling strategy, which
significantly improves our model's performance and efficiency. This sampling
strategy for flow step can be easily applied to existing flow matching based
models without retraining. Our design allows faster training and achieves an
inference RTF of 0.15, which is greatly improved compared to state-of-the-art
diffusion-based TTS models. Trained on a public 100K hours multilingual
dataset, our Fairytaler Fakes Fluent and Faithful speech with Flow matching
(F5-TTS) exhibits highly natural and expressive zero-shot ability, seamless
code-switching capability, and speed control efficiency. Demo samples can be
found at https://SWivid.github.io/F5-TTS. We release all code and checkpoints
to promote community development.
MLE-bench: Evaluating Machine Learning Agents on Machine Learning
  Engineering
Authors: name: Jun Shern Chan, name: Neil Chowdhury, name: Oliver Jaffe, name: James Aung, name: Dane Sherburn, name: Evan Mays, name: Giulio Starace, name: Kevin Liu, name: Leon Maksin, name: Tejal Patwardhan, name: Lilian Weng, name: Aleksander Mądry
Published: 2024-10-09 17:34:27

We introduce MLE-bench, a benchmark for measuring how well AI agents perform
at machine learning engineering. To this end, we curate 75 ML
engineering-related competitions from Kaggle, creating a diverse set of
challenging tasks that test real-world ML engineering skills such as training
models, preparing datasets, and running experiments. We establish human
baselines for each competition using Kaggle's publicly available leaderboards.
We use open-source agent scaffolds to evaluate several frontier language models
on our benchmark, finding that the best-performing setup--OpenAI's o1-preview
with AIDE scaffolding--achieves at least the level of a Kaggle bronze medal in
16.9% of competitions. In addition to our main results, we investigate various
forms of resource scaling for AI agents and the impact of contamination from
pre-training. We open-source our benchmark code (github.com/openai/mle-bench/)
to facilitate future research in understanding the ML engineering capabilities
of AI agents.
MentalArena: Self-play Training of Language Models for Diagnosis and
  Treatment of Mental Health Disorders
Authors: name: Cheng Li, name: May Fung, name: Qingyun Wang, name: Chi Han, name: Manling Li, name: Jindong Wang, name: Heng Ji
Published: 2024-10-09 13:06:40

Mental health disorders are one of the most serious diseases in the world.
Most people with such a disease lack access to adequate care, which highlights
the importance of training models for the diagnosis and treatment of mental
health disorders. However, in the mental health domain, privacy concerns limit
the accessibility of personalized treatment data, making it challenging to
build powerful models. In this paper, we introduce MentalArena, a self-play
framework to train language models by generating domain-specific personalized
data, where we obtain a better model capable of making a personalized diagnosis
and treatment (as a therapist) and providing information (as a patient). To
accurately model human-like mental health patients, we devise Symptom Encoder,
which simulates a real patient from both cognition and behavior perspectives.
To address intent bias during patient-therapist interactions, we propose
Symptom Decoder to compare diagnosed symptoms with encoded symptoms, and
dynamically manage the dialogue between patient and therapist according to the
identified deviations. We evaluated MentalArena against 6 benchmarks, including
biomedicalQA and mental health tasks, compared to 6 advanced models. Our
models, fine-tuned on both GPT-3.5 and Llama-3-8b, significantly outperform
their counterparts, including GPT-4o. We hope that our work can inspire future
research on personalized care. Code is available in
https://github.com/Scarelette/MentalArena/tree/main
TextToon: Real-Time Text Toonify Head Avatar from Single Video
Authors: name: Luchuan Song, name: Lele Chen, name: Celong Liu, name: Pinxin Liu, name: Chenliang Xu
Published: 2024-09-23 15:04:45

We propose TextToon, a method to generate a drivable toonified avatar. Given
a short monocular video sequence and a written instruction about the avatar
style, our model can generate a high-fidelity toonified avatar that can be
driven in real-time by another video with arbitrary identities. Existing
related works heavily rely on multi-view modeling to recover geometry via
texture embeddings, presented in a static manner, leading to control
limitations. The multi-view video input also makes it difficult to deploy these
models in real-world applications. To address these issues, we adopt a
conditional embedding Tri-plane to learn realistic and stylized facial
representations in a Gaussian deformation field. Additionally, we expand the
stylization capabilities of 3D Gaussian Splatting by introducing an adaptive
pixel-translation neural network and leveraging patch-aware contrastive
learning to achieve high-quality images. To push our work into consumer
applications, we develop a real-time system that can operate at 48 FPS on a GPU
machine and 15-18 FPS on a mobile machine. Extensive experiments demonstrate
the efficacy of our approach in generating textual avatars over existing
methods in terms of quality and real-time animation. Please refer to our
project page for more details: https://songluchuan.github.io/TextToon/.
CursorCore: Assist Programming through Aligning Anything
Authors: name: Hao Jiang, name: Qi Liu, name: Rui Li, name: Shengyu Ye, name: Shijin Wang
Published: 2024-10-09 15:45:52

Large language models have been successfully applied to programming
assistance tasks, such as code completion, code insertion, and instructional
code editing. However, these applications remain insufficiently automated and
struggle to effectively integrate various types of information during the
programming process, including coding history, current code, and user
instructions. In this work, we propose a new conversational framework that
comprehensively integrates these information sources, collect data to train our
models and evaluate their performance. Firstly, to thoroughly evaluate how well
models align with different types of information and the quality of their
outputs, we introduce a new benchmark, APEval (Assist Programming Eval), to
comprehensively assess the performance of models in programming assistance
tasks. Then, for data collection, we develop a data generation pipeline,
Programming-Instruct, which synthesizes training data from diverse sources,
such as GitHub and online judge platforms. This pipeline can automatically
generate various types of messages throughout the programming process. Finally,
using this pipeline, we generate 219K samples, fine-tune multiple models, and
develop the CursorCore series. We show that CursorCore outperforms other models
of comparable size. This framework unifies applications such as inline chat and
automated editing, contributes to the advancement of coding assistants. Code,
models and data are freely available at
https://github.com/TechxGenus/CursorCore.